Context-Aware Model of Abstractive Text Summarization for Research Articles Gopinath Dineshnatha, Selvaraj.Saraswathib a Department of Computer Science Engineering, Pondicherry Engineering College, Pillaichavady, Puducherry, 605014, India. b Department of Information Technology, Pondicherry Engineering College, Pillaichavady, Puducherry, 605014, India. Abstract Research article comprises of different sections each holds its own characteristic domain information. Summarization of entire article from multiple documents of multiple sections in precise form with special focus to contextual information is tedious. We proposed context-aware model to extract contextual texts from research article by utilizing multi-document directed graph for contextual matching phrases We customized extractive summarization for abstractive text summarization with lucid-information as prime criterion. Decision matrix with elitism identification further fine tunes the abstractive text summary and outperforms at sentence level Rouge-L measures 9.32 and summary level measures 89.65. Keywords1 Glowworm Swarm Optimization, Analytical Hierarchical Processing (AHP), Skip Gram Model, Word2vectors, Maximum Marginal Relevance, Log-Likelihood Rate,N-Gram. 1. Introduction in two ways; single-document summaries produce a summary from a given single source and multi-document summaries in which Modern days, internet becomes the integral part different but related documents are summarized of human and act as information highway. The by comprises only the vital materials or main primary source of information in digital world ideas in a document in less space. is Internet and it is boon for academicians, There is a vast difference between automatic bloggers, students and researcher fraternity. multi-document summarization of generic texts Information available in Internet comprises of to that of scientific articles. The major massive flow of information, which makes difference [2] between generic-text and research retrieval process complex with respect to article summarization is; research article context-specific content. Scientific article consists of different section namely abstract, prevailing now with ocean of research domains introduction, literature survey, methods, results makes difficult to scholar cope-up, grasp and and discussions, whereas generic text’s scope is streamline documents relevant to their interest. extracted from first few sentences in first Query based search [1] for specific domain also paragraphs and entire section holds at fetch many relevant articles that is difficult task maximum 500 words. to categorize surpass human processing In general, abstract and citation texts in capabilities. In such scenario, automatic text scientific articles are considered for automatic summarization of articles is fruitful solution in summarization system. terms of reducing time effort for reviewing Abstract section[3] is biased to author entire articles and grab gist of information findings, author’s own contribution, and enclosed in it. Basically, summaries generation evaluation metrics. In simpler way, abstract ACI’21: Workshop on Advances in Computational Intelligence at ISIC 2021, February 25-27,2021, Delhi, India. Email: dinesh.gopinath60@gmail.com(G.Dineshnath); swathi@pec.edu (S.Saraswathi); :0000-0003-0026-1932(A.1) ©️2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org) outlines the domain and list of findings in crisp techniques. Our main focus is Context aware manner depends upon the type of articles information inference from multiple (review/original). documents. Citation sensitive or citation-based summaries [4] is another type of Scientific article 2. Literature review summarization, major task in summary production is clear-cut distinction of cited and Lloret et al. [7] have applied both extractive and non-cited text is performed. Citation summary abstractive summarization procedures for system performs categorization of every sentence and labeled to citation or non-citation scientific article abstracts. The extractive one. Later, evaluation measures based on summarizer (compendium E) is developed to similarity between each sentence in the perform conventional preprocessing such as reference article and citation sentences and then breaking sentences, assigning tokens, grouped it into one of two classes: cited or non- stemming, lemmatization and PNG markers, cited. Abstractive Multi document tagging and removing duplicates at various sentence levels. [8]A mixture of both extractive summarization [5] selectively picks up either and abstractive technique (compendium E−A) first sentence of abstract or introduction of a is developed to support compendium E as base paper since it is comprised of background to incorporates sorted information which are information of research topic. Construction of appropriate title for article involves relevant. Relevancy identification with respect interpretation and integration of the concepts to every sentences, assigns a score that from multiple sentences of the abstract. Apart emphasize its importance based on code from that there exist multiple challenging issues quantity principle (CQP)[9] compendium E−A like content organization, sentence derives abstractive summary by utilizing top compression and fusion, and paraphrasing ranked sentences with chronological ordering. Saggion [11] utilized pretrain models sentences. All summarization system should meet the for learning and transformations for the summary length constraints as and other problem of abstract generation. The initial parameters specified by the user or summary generation from abstracts are generated and transformed to model based summarization system is known as controllable learning. The learning models assists with summarization. Controllable summarization [6] is the main criteria in summarization system examples from corpus. Further, abstracts are which specifies the length of summary gathered from GATE [12]and Weka [13] generation in accordance to the entities on environment. Abstractive text summarization which it focuses on and mimics the source’s also known as natural language generation in natural language processing Paraphrasing of style. User may define the high-level attributes for summary generation. Summary generation sentences is also another important criterion in is controlled by specific control variables, natural language generation Paraphrasing of length, and source style, entities of interest and sentences [14] involves substitution of relevant summarizing only remaining portions of the verbatim and modifying the direct to indirect document. For instance, blog summarization, speech or vice versa. The vector representation the primary thing is to derive representative is purely focus on various sources of features words from comments and then selection of namely LDA, D2V, W2V and encoding schemes[15]..LDA[16] explores semantic paramount sentences from the blog post which consists of representative words. associations, D2V vectors finds contextual Context aware components are usually word vectors along with documents. Contextual meant task of inferring contextual information. aware model phase is concerned with Contextual information detection might be contextual theme and dependency phrase detection ranging from topic community, extraction from multi documents using directed paragraphs analysis, sentences and words by graph. minimal spanning tree is constructed for statistical computation measures. The most edges algorithm using Chu Liu Edmonds. well-known computation is Set-Calculus (CLE)[17] Knowledge base or ontology- based techniques to find sentence similarity via context aware model useful for domain Union, Jaccard Co-efficient, Cosine -Similarity classification and annotation.[18]Evaluation of document summarization, Document measures followed by normalization Understanding conference (DUC)[19] is used to slide over word vectors for prediction benchmark datasets is used generally. Various of context vectors. Skip gram models skips the datasets such as TAC[20],shared task for text selection of common words rather than processing and document summarization contextual words. N gram models hold Similarly, DPIL[21] for paraphrasing Indian predefined size ‘n’ that triggers the selection of languages for text summarization. contextual words upon size limited to ‘n’. Both skip gram and N gram models are desirable notion to pick keywords in context based on W(Vi) = (1 – d) + d ∗ their lexicography collocations. The Proposed overlap(si ,sj ) (1) ∑Vj∈ln(Vi) W(Vj) diagram is given in figure-1. ∑ Vk∈Out(Vj )overlap(sj ,sk) . The edge weights for nodes are assigned using status score on the basis of inwards and outwards edges, W(Vi) represents status score assigned to vertex Vi. In (Vi) and Out (Vj) are inwards and outwards edges, points from particular node. After several iterations, each sentence in the document is assigned with a score. The top-n sentences are selected and ranked which constructs the summary for the document. There exist some dependent phrases in graph. The dependent phrases are cyclic in nature and some disjoint nodes. Such, disjoint nodes in graph are connected using CLE algorithm. On basis of lexical contexts, proposed procedure performs well than traditional keyword-based algorithms. We enhance the extractive summary production by adding co-occurrence measures to ensure Concept-based Point wise Mutual Information. (CPMI) CPMI weighs the different section in paragraphs gradually it weights decreases from beginning of paragraph to end of paragraph in document. CPMI measures support distributional semantics among phrases. CPMI weightage scheme H(ƥ) is expressed in equation 2. H(ƥ) log B (2) C − positive constant ƥ ∗ B, if ƥ < − ( ) log C = , { 1 otherwise } Figure 1: Proposed Context aware model for Abstractive Summarization 3. Context Aware Model Phase Context aware component model proposed 3.1. Contextual matching phrases follows contextual keyword interpretation, topic detection and topic clusters formation. Contextual matching phrase from multi Context aware component model also documents is proposed to retrieve thematic determines vector manipulation by bag of portions with similar sentence phrases words and skip gram key terms with respect to sequentially from one document to another. The specific documents. One hot encoding scheme graph based contextual word is intuitive way to Formula for Extraction of Eq customized to produce extractive to abstractive Features. n.o: summary.in Table-1. The optimal features and Number of title Features (3) their convergence in Adaptive Glowworm 𝑇𝑖 = average length(Title) Optimization (AGWO) is discussed in section 1 − e –α (4) Tag sum = 3.4. 1 + eα ∑L(Si) (5) (Sbi) = Lmax Table 1 Optimal Features Extraction. TW(𝑆𝑖) (6) Dsm(𝑆𝑖) = 𝑇𝑀𝑎𝑥 represent sentences as node and corresponding contextual word as vertices. The path projecting Features Glowworm from node to node via outcoming and incoming optimization vertices provides a notion either matching Title feature Luciferin update target phrases or discriminating phrases. phase subsequent dependent phrases need to be Named entity Movement phase included in directed graph. The procedure for recognition and contextual theme and dependency phrases Tagging extraction is shown below. Sentence boundary Neighborhood Phase Step 1 Accept text and store it in text buffer. Distributional Words and conceptual Step 2 new word falls into below categories. Semantic Analysis level Step 2 a): If first word then adds to graph G. Step 2 b): If fresh word then appends to G. Step 3 Go to step 2 until words overlap. 3.3. Vector Formulation Step 4 If overlap, status score using (1). Step 5 Extract similar texts and update in G. Word embeddings are feature vectors that Step 6 Construct Digraph using CLE. represent words holds the property that similar Step 7 Do updates to infer adjacent edges. words have similar feature vectors. The Step 8: Output the phrases. question might rise in mind where the embeddings come from. The response is 3.2. Feature Extraction (again): they are learned from data. Several algorithms exist to learn word embeddings. We The feature extraction for document consider only one of them: word2vec, and sole summarization includes title feature, proper version of word2vec called skip-gram, which is nouns, Named Entities Recognition (NER) and well-known and currently utilized in practice. parts of speech tagging, sentence boundary Word embedding learning, our goal is to build analysis and distributional semantic analysis. a model which we can use to convert a one-hot Title feature scoring scheme is based on the encoding of a word into a word embedding. Let ratio of mean number of titles present to that of our dictionary contain 10,000 words or Giga- average length of title. The formula for title word Corpus. Skip-gram model performs for feature is expressed in equation. (3). The proper given sentence, selection of a word is feed into nouns are generally recognized as title words classifier, and predict words before and after the and minimum number of words to accept as selected word in a fixed window. Negative title. NER marks or labels the salient sentences sampling provides better vectors for frequent which is considered for summary. The scoring words with low dimension. scheme is expressed in equation. (4). where α = Latent Dirichlet (LD) allocation is a (t(s) − µ) /σ (sigmoid function) aggregates possibility-based mechanism viable for mean count of regular expressions, case level assortments, for example, text assortments. LD and numeric literals. Sentence boundary consolidates the documents as a blend of shifted calculation is expressed in equation (5). topics; every unit involves words that have a Distributional Semantic Analysis weighs spun affiliation that exists between them. Also, thematic concepts to find word co-occurrence. word choice simply dependent on the numerical It is scored using formula. (6). Column one idea of likelihood. Recursively determining the represents features, similarly column two interaction of themes and words is done for the represents formulae used for computation and phase of a lonely record or a large number of column three indicates optimal features documents. At long last, yields the record Glowworm Optimization (GO)[22] which comprises of different subjects. comprises of three phases namely Luciferin LD allocation algorithm performs the update phase, Neighborhood phase and following: movement phase. Adaptive Glowworm 1) Determine the number N of words in the Optimization (AGO) is proposed for tailor- document concerning probability distribution is made features to acquire vectors or extract Poisson. features to frame the summary. The 2) Pick a merge of focuses for the report optimization principle is based on five features from a predefined set of K subjects as and their application phase is listed in Table-1. demonstrated by the Dirichlet movement. Sentence position is additional feature to 3) Produce individually word in the list of revisit sentence with appropriate ordering. terminology as follows language Vocabulary Positioning of sentences which is most vital (V). part in the summary generation have higher a) Choose a subject; weights. The feature associated with sentence b) Choose a word in this subject. length; hence we have minimal set of 25 words to accept as a sentence. F5 feature is in 3.4. Sentence Ranking movement phase of Glow worm optimization with lucerifin value or luminous quotient, affine towards the similar topics. Sentence ranking phase, chiefly performs Luciferin update phase, sentences are identification of prominent sentences with concatenated with respect to the relevancy. pertinent information and free from Relevancy is determined by feature with redundancy. It selects topmost sentences from respect to title and all sentences in document. documents and produces summary with Luciferin update phase, movement phase and application of traditional maximization neighbor phase are expressed as equation in (7) algorithm like EM.[23]. finally produces (8) and (9) respectively Luciferin extractive summary. However, Extractive enhancement(ʋ) depends upon Proper Nouns. summary lacks positional placement of sentences. hence there is a need to revisit J(xi(t))-objective function which maximize sentence positions.[24]. weights of every proper nouns. Luciferin decay constant gradually decreases when common 3.5. Extractive Summary noun exists. Movement Phase, forms local clusters based Extractive summary is created with top n on decision range. Sentences are of similar sentences for research articles summarization contexts likely to move based on entity-tagging Latter summary is transformed to decision features. Finally, neighbor Phase performs matrix. Decision Matrix will keep track of chronological sorting of clustered sentences to extractive summary to make compatible for produces summary. abstractive text summary with usage measure and penalty measure. Hence, optimization 4. Results and Discussions algorithm is used to remove exact replica of original text produced in extractive summary. The extractive summarization shows better Co-reference resolution is also handled in results with proposed procedure, the summary abstractive text summary generation. produced purely relies on lexical features and Meanwhile construction of cosine similarity surpass traditional keyword ranking schemes. was rapid, useful, and seems reasonable. yellow color denotes dependency phrases and green color denotes contextual theme. The 3.6. Optimization output of context aware component of extractive summarization is shown as well as output of abstractive text summarization is also li(t + 1) = (1 − 𝜌)li(t) + ʋJ (xi (t + 1)); (7) shown. Ni(t) = {j: dij < rid(t); li (t) < lj (t)} (8) 𝑟𝑖𝑑(𝑡 + 1) = 𝑚𝑖𝑛{𝑟𝑠, 𝑚𝑎𝑥{0, (9) 𝑟𝑖𝑑(𝑡) + 𝛽(ǹ𝑡 − |𝑁𝑝(𝑡)|}}; 4.1. Training and Testing Output of Context aware For training purpose, Document understanding Extractive and Abstractive Conference (DUC) data set taken into Summarization considerations. The precise explanation of DUC data sets and DUC data is customized, which is free from least significant words or Extractive Summary stop words according to port-stemmer’s A large number of methods applied in algorithm. Recall oriented understudy Gist the field of extractive summarization evaluation, Rouge(R) is also considered for over the years. Scoring sentences for evaluation. R falls into many variants like R- such summary is tedious task. Many unigram, R-bigram, R-Longest common researchers putting so much effort to Subsequence and R-N gram classes. Multiple documents of artificial intelligence improve the quality of summary. domain for testing and performed various Document summarization focus both measures like R-1, R-2, and R-L (Longest quality and coverage of content. Common Subsequence) scoring. At Sentence- Clustering of sentences in document level, computes longest common subsequence summarization shown promising results (LCS) between two pieces of text ignores new to discover topics. A Fuzzy oriented lines and summary-level, newlines in the text clustering for summarization of multi- are interpreted as sentence boundaries, and the documents. Compendium- a summarizer LCS is computed between each pair of tool, generates relevant summary free reference and candidate sentences, and their from redundancy Collabsum clustering results are tabulated below in table-2. process both inter and intra document The proposed AGO performs well in sentence and summary level than traditional methods. relationship and forms clusters. Clusters similarly, contextual theme detection also out in turn apply graph based ranking performs than traditional schemes like lexrank, methodology. FEOM…genetic maximum relevance, loglikelihood ranking algorithm…graph -based [25],[26],[27] and other centrality measures as approach…probabilistic model. baseline evaluation. Table 2 Results Comparison Abstractive summary Methods R-1 R-2 R-L R-L Various types of Sentence clustering Sentence Summary techniques applied to document level level summarization Sentence scoring, topic Proposed 0.4611 0.1342 0.932 0.89 coverage, relevant sentences and AGO summarization quality are main MMR 0.3716 0.0757 0.16 0.80 components in summary production. LEX 0.4030 0.0913 0.69 0.53 Clustering algorithms for sentence scoring and grouping similar sentences Proposed 0.4297 0.0835 0.12 0.83 according to topics conveyed in contextual document. A fuzzy based, evolutionary theme based clustering also successfully Tf-idf 0.3639 0.0736 0.14 0.81 applied in conjunction with other graph- LLR 0.3975 0.0850 0.084 0.64 based approaches to provide summary. 5. Conclusion References Abstractive text summarization for research [1] Shafiei Bavani, Elaheh, articles generates sentences individually using Mohammad Ebrahimi, Raymond glowworm optimization with six associated Wong, and Fang Chen. "A query- features. In addition, decision matrix with based summarization service from elitism identification is formulated to choose multiple news sources." In 2016 summary sentences from both extractive IEEE International Conference on summary sentences and abstractive summary Services Computing (SCC), IEEE, sentences with consistency as necessary (2016): pp.42-49. doi: condition. Extractive summary is reduced to 10.1109/SCC.2016.13. more than 80% to generate abstractive [2] Bharti, Santosh Kumar, Korra summary. Extractive summary with 661 word Sathya Babu, Anima Pradhan, S. tokens is produced as output in first phase. Devi, T. E. Priya, E. Orhorhoro, O. Later, decision matrix with Elitism Orhorhoro, V. Atumah, E. Baruah, identification produces abstractive summary and P. Konwar. "Automatic with 84 tokens is obtained as final output. keyword extraction for text Proposed multi-document directed graph summarization in multi-document contextual matching phrases, Rouge-L e-newspapers articles." European measures in sentence level is 12.08 and Rouge- Journal of Advances in L measures in summary level is 83.95 for Engineering and Technology extractive summary. Similarly, Rouge-L (2017): 4, pp.410-427. measures in sentence level is 9.32 and Rouge-L [3] Chowdhury, S. M., and Mazharul measures in summary level is 89.68 for Hoque. "A Review Paper on abstractive summary. A novel model has been Comparison of Different implemented to be ample enough to provide Algorithm Used in Text multi objectives and to convince the Summarization." Intelligent Data instantaneous needs. Ultimately, this study will Communication Technologies and inspire many researchers to further explore and Internet of Things: ICICI 2019 38 apply the various types of Swarm intelligence (2019): 114. while solving the summarization tasks, [4] Cohan, Arman, and Nazli specifically in the abstractive text Goharian. "Scientific article summarization (ATS) field. summarization using citation- context and article's discourse 6. Future works structure." (2017). arXiv preprint arXiv:1704.06619 [5] Nouf Ibrahim Altmami, Mohamed Decision matrix performs combination of El Bachir Menai, “Automatic Sentences from extractive summary are summarization of scientific assessed and deemed to be fit for abstractive articles: A survey.” Journal of King summary are analyzed in conjunction with Saud University-Computer and input from optimization algorithm with Information Sciences, (2020) associated six features. Selection of best doi.:10.1016/j.jksuci.2020.04.020. sentences and worst sentences based on their [6] Fan, Angela, David Grangier, and usage and penalty is awarded to compose Michael Auli. "Controllable summary. Global decision matrix performs abstractive summarization." arXiv elitism identification (algorithm) and outputs preprint arXiv:1711.05217 (2017). sentences with sentence flow as criterion. [7] Lloret, Elena, María Teresa Romá- However, decision matrix follows Analytical Ferri, and Manuel Palomar. Hierarchical Processing (AHP) [28] with user "COMPENDIUM: A text defined decision values and their decisions are summarization system for normalized. We can extend the normalized generating abstracts of research vectors by using fuzzy [29] based membership papers." Data & Knowledge assessment as stated by Charugupta,et.al.[30] Engineering (2013):88, pp.164- [17] Nizami, Muhammad, and Ayu 175. Purwarianti. "Modification of Chu- [8] Ferrández, Oscar, Daniel Micol, Rafael Liu/Edmonds algorithm and MIRA Munoz, and Manuel Palomar. "A learning algorithm for dependency perspective-based approach for solving parser on Indonesian language." In textual entailment recognition." In 2017 International Conference on Proceedings of the ACL-PASCAL Advanced Informatics, Concepts, workshop on textual entailment and Theory, and Applications (ICAICTA), paraphrasing, (2007). pp.66-71. IEEE (2017) pp.1-6. [9] Gardenfors, Peter. The geometry of [18]Malik, Sonika, and Sarika Jain. meaning: Semantics based on "Ontology based context aware conceptual spaces. MIT press, 2014. model." In 2017 International [10]Luhn, Hans Peter. "The automatic Conference on Computational creation of literature abstracts." IBM Intelligence in Data Science (ICCIDS), Journal of research and development IEEE (2017). pp.1-6. (1958): 2, pp.159-165. [19]Sanchez-Gomez, Jesus M., Miguel A. [11]Saggion, Horacio. "Learning predicate Vega-Rodríguez, and Carlos J. Pérez. insertion rules for document "Extractive multi-document text abstracting." In International summarization using a multi-objective Conference on Intelligent Text artificial bee colony optimization Processing and Computational approach." Knowledge-Based Systems Linguistics, Springer (2011) pp.301- (2018): 159, pp.1-8. 312. [20]ShafieiBavani, Elaheh, Mohammad [12]Maynard, Diana, Valentin Tablan, Ebrahimi, Raymond Wong, and Fang Hamish Cunningham, Cristian Ursu, Chen. "A graph-theoretic summary Horacio Saggion, Kalina Bontcheva, evaluation for rouge." In Proceedings and Yorick Wilks. "Architectural of the 2018 Conference on Empirical elements of language engineering Methods in Natural Language robustness." Natural Language Processing (2018): pp.762-767. Engineering (2002): 8, pp.257-274. [21]Anand Kumar M., Singh S., Kavirajan [13]Witten, Ian H., and Eibe Frank. "Data B. and Soman K.P. “Shared Task on mining: practical machine learning Detecting Paraphrases in Indian tools and techniques with Java Languages (DPIL): An Overview”. In: implementations." ACM Sigmod Majumder P., Mitra M., Mehta P., Record (2002):31, pp.76-77. Sankhavara J. (eds) Text Processing. [14]Sethi, Nandini, Prateek Agrawal, Vishu FIRE 2016. Lecture Notes in Computer Madaan, and Sanjay Kumar Singh. "A Science, Springer (2018):10478. novel approach to paraphrase Hindi [22]Alphonsa, MM Annie, and P. sentences using natural language Amudhavalli. "Genetically modified processing" Indian Journal of Science glowworm swarm optimization based and Technology (2016):9(28), pp.1-6. privacy preservation in cloud [15]Alguliyev, Rasim M., Ramiz M. computing for healthcare sector." Aliguliyev, Nijat R. Isazade, Asad Evolutionary Intelligence (2018):11 Abdi, and Norisma Idris. “COSUM: pp: 101-116. Text summarization based on [23]Janani, R., and S. Vijayarani. "Text clustering and optimization.” Expert document clustering using spectral Systems (2019):36, clustering algorithm with particle doi:10.1111/exsy.12340. swarm optimization." Expert Systems [16]Gupta, Monika, and Parul Gupta. with Applications, Elsevier (2019) 134, "Research and implementation of event pp.192-200. extraction from twitter using LDA and [24] Xu, Song, Haoran Li, Peng Yuan, scoring function." International Journal Youzheng Wu, Xiaodong He, and of Information Technology (2019):11, Bowen Zhou. "Self-Attention Guided pp.365-371. Copy Mechanism for Abstractive Summarization." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020): pp.1355-1362. [25] Weng, Shi-Yan, Tien-Hong Lo, and Berlin Chen. "An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features." In 2020 28th European Signal Processing Conference (EUSIPCO), IEEE (2021): pp.316-320. [26] Mallick, Chirantana, Ajit Kumar Das, Madhurima Dutta, Asit Kumar Das, and Apurba Sarkar. "Graph-based text summarization using modified TextRank." In Soft computing in data analytics, Springer (2019): pp.137-146. [27] Sabbah, Thabit, Ali Selamat, Md Hafiz Selamat, Fawaz S. Al-Anzi, Enrique Herrera Viedma, Ondrej Krejcar, and Hamido Fujita. "Modified frequency- based term weighting schemes for text classification." Applied Soft Computing (2017):206, pp.58 193. [28] Tofighy, Seyyed Mohsen, Ram Gopal Raj, and Hamid Haj Seyyed Javad. "AHP techniques for Persian text summarization." Malaysian Journal of Computer Science (2013): 26, pp. 1-8. [29] Bansal, Neha, Arun Sharma, and R. K. Singh. "Fuzzy AHP approach for legal judgement summarization." Journal of Management Analytics (2019):6, pp.323-340. [30] Gupta, Charu, Amita Jain, and Nisheeth Joshi. "Fuzzy logic in natural language processing–a closer view." Procedia computer science (2018):132 pp.1375- 1384.