=Paper=
{{Paper
|id=Vol-2414/paper25
|storemode=property
|title=NJU@CL-SciSumm-19
|pdfUrl=https://ceur-ws.org/Vol-2414/paper25.pdf
|volume=Vol-2414
|authors=Hyonil Kim,Shiyan Ou
|dblpUrl=https://dblp.org/rec/conf/sigir/KimO19
}}
==NJU@CL-SciSumm-19==
<pdf width="1500px">https://ceur-ws.org/Vol-2414/paper25.pdf</pdf>
<pre>
                            NJU@CL-SciSumm-19

            Hyonil Kim1,3[0000-0002-6380-9507] and Shiyan Ou1,2 [0000-0001-8617-6987]
          1 School of Information Management, Nanjing University, Nanjing, China
                                2 oushiyan@nju.edu.cn
                                  3 kimhyonil@126.com




       Abstract. Cited text identification is helpful for meaningful scientific literature
       summarization. In this paper, we introduces our system submitted to the CL-
       SciSumm 2019 Shared Task 1A. Our system have two stages: similarity-based
       ranking and supervised listwise ranking. Firstly, we select the top-5 sentences per
       a citation text, due to the modified Jaccard similarity. Secondly, these top-5 se-
       lected sentences are proceeded to rank by a CiteListNet (listwise ranking model
       based on deep learning). Our experiments showed that our proposed method out-
       performed other prior methods on the CL-SciSumm 2017 test dataset.

       Keywords: Cited Text Identification, Cited Text, Listwise Ranking, Citation
       Content Analysis, Text Similarity.


1      INTRODUCTION

Automatic summarization of academic paper may be a very effective solution to avoid
the information overload of researchers and to understand the state-of-the-art of the
research topic.
    The CL-SciSumm shared tasks explore the solutions for the making a comprehensi-
ble summary of an academic paper given its citation text. These tasks focuses on the
sentence-level cited text information to perform the summarization of a paper. To this
end, the identification of the cited text should be done. CL-SciSumm Shared Task 1A
is just to identify the spans of cited text in reference paper (RP) that contain the given
citation text of its citing paper (CP).
    In this paper, we use various similarity metrics to evaluate the similarity between a
citation text and its candidate cited sentence, and adopt listwise ranking algorithm to
train our ranking model.


2      RELATED WORK

Most of previous studies regarded the identification of cited texts as a classification
problem and thus used some machine learning algorithms like SVM, Random Forest,
CNN to train text classifiers. To build the classifiers, various features were explored by
researchers. Ma et al. (2018) chose Jaccard similarity, cosine similarity and some posi-
2


tion information as features, and trained four classifiers including Decision Tree, Lo-
gistic Regression and SVM.[9] Finally they used a weighted voting method to combine
the categorization results of the four classifiers and achieved the best performance in
CL-SciSumm 2017 competition. Yeh et al. (2017) considered some lexical features,
knowledge-based features, corpus-based features, syntactic features, surface features to
represent the feature vector and adopted a majority voting method to combine the re-
sults of the six classifiers like KNN, Decision Tree, Logistic Regression, Naive Bayes,
SVM and Random Forest. They got the F value of 14.9% by running their system on
the corpus of the CL-SciSumm 2016 competition.[3]
   There are two main issues in the categorization-based methods: local ranking and
class-imbalanced data. On the one hand, the cited text identification problem should be
regarded as a ranking problem rather than a classification one because we only intent
to choose the sentence(s) that contains more similar content with the citation sen-
tence(s) compared to other sentences. On the other hand, there is only few sentences
(usually not more than five) to be cited sentences in a target paper. Sometimes the ratio
of the negative and positive sample in a corpus is even greater than 150. Ma et al. (2018)
used Nearest Neighbor (NN) rule (Wilson, 1972) to reduce data imbalance and in-
creased the F1-score from 11.8% to 12.5%.[9]
   With respect to the ranking-based cited text identification, a few studies have been
done. Dipankar et al. (2017) ranked the sentences in a target paper according to the
cosine similarity between each candidate sentence and the citation sentences to select
the top five sentences as the cited sentences.[2] However, this unsupervised method did
not obtain reasonable performance. Therefore, we proposed a listwise ranking method
for identifying cited sentences, which is supervised method trained by a deep learning
mechanism.


3      Methodology

In this study, we regarded cited text identification as a ranking problem, and proposed
a ranking-based method to identify citation sentences based on deep learning. This
method includes two stages of ranking: a similarity-based unsupervised ranking and a
supervised listwise ranking. Since the cited text was deemed to contain more similar
content with the cited text than other sentences in the same paper, we first ranked all
the sentences in a reference paper according to each sentence’s similarity with a cited
text. Then we choose top K sentences to create a subset of the given train corpus for the
second stage ranking, while the Kth sentence obtained the best F-value according to the
given training corpus. In the second stage, a listwise ranking model was trained on the
subset training corpus to rank the K sentences and then top N sentences (N<K) were
selected as the cited sentences.


3.1    Similarity-based Ranking
In this section, we explored different similarity metrics between two texts to rank all
the sentences in a cited text (named as candidate sentences) for a specific citation text
                                                                                         3


in one of its citing paper, whereas a citation text may contain one or more sentences.
We considered five kinds of similarity metrics, including TFIDF-based cosine similar-
ity, word embedding-based cosine similarity, SVM Kernel functions-based cosine sim-
ilarity, Jaccard-like similarity and BM25.


Cosine Similarity based on TFIDF Weighted Vector Space Model. In this kind of
similarity metrics, N-grams were extracted from a text as its features and represented
the text as a TFIDF weighted feature vector based on Vector Space Model and calcu-
lated the cosine similarity between two feature vectors. We tested two TFIDF-based
cosine similarity metrics: when N=1 and when N=2 respectively for N-grams.




                Fig. 1. Two-stage ranking for the identification of cited text.


Cosine Similarity based on Word Embedding. In this kind of similarity metrics, a
text was represented as a text vector based on word embedding. First, the vector of each
word in a text was trained with Word2vec. Then three kinds of text vectors were calcu-
lated based on the word vectors using different weighting mechanisms, TF-weighted
average of the word vectors (TF-AWV), TFIDF-weighted average (TFIDF-AWV), and
T-statistics-weighted average (Tstat-AWV). Here, the T-statistic of a word refers to the
T-test statistics of the hypothesis that whether the word appears in a text or not is inde-
pendent to whether or not the text is a cited text.

Cosine Similarity based on SVM kernel functions. As we know, a linear inseparable
sample can become linearly separable by projecting a low-dimensional space to a high-
dimensional one. Thus we considered to transform the TFIDF-weighted average word
vector (TFIDF-AWV) to a higher-dimensional space by using three SVM kernel func-
tions, so as to make a text more distinguishable from others. Thus three kinds of kernel
functions-based cosine similarity were calculated with the following equation based on
three kernel functions respectively, i.e. 2-dimension polynomial, 3-dimension polyno-
mial function and RBF function.
                                          𝜙(𝐴)∙𝜙(𝐵)                𝐾(𝐴,𝐵)
            𝑐𝑜𝑠(𝜙(𝐴), 𝜙(𝐵)) =                              =                           (1)
                                   √𝜙(𝐴)∙𝜙(𝐴)∙√𝜙(𝐵)∙𝜙(𝐵)       √𝐾(𝐴,𝐴)∙√𝐾(𝐵,𝐵)
4


Where 𝜙(∙) denotes a mapping function, by which a vector space can be mapped to
another space, 𝐾(∙,∙) refers to a kernel function that follows Mercer’s condition[10],
and A (or B) refers to the vectors of the original space.

Cosine Similarity based on SVM Kernel Functions. As we know, a linear insepara-
ble sample can become linearly separable by projecting a low-dimensional space to a
high-dimensional one. Thus we considered to transform the TFIDF-weighted average
word vector (TFIDF-AWV[11]) to a higher-dimensional space by using three SVM
kernel functions, so as to make a text more distinguishable from others. Thus three
kinds of kernel functions-based cosine similarity were calculated with the following
equation based on three kernel functions respectively, i.e. 2-dimension polynomial, 3-
dimension polynomial function and RBF function.

Jaccard-like Similarity. A citation text in a citing paper and each candidate sentence
in a reference paper can be regarded respectively as a set of N-grams (N=1, 2, 3). We
used some Jaccard-like similarities to calculate the overlap between the N-gram set of
the citation text and the one of each candidate cited sentence. In addition to the standard
Jaccard similarity metric, two variations, MJS1 (see eq. 3) and MJS2 (see eq. 4) were
also considered.
                                              |𝐴∩𝐵|           |𝐴∩𝐵|
                         𝐽𝑎𝑐𝑐𝑎𝑟𝑑(𝐴, 𝐵) =              = |𝐴|+|𝐵|−|𝐴∩𝐵|                       (2)
                                              |𝐴∪𝐵|

                                                      |𝐴∩𝐵|
                                   𝑀𝐽𝑆1(𝐴, 𝐵) =                                             (3)
                                                       |𝐴|

                                                      |𝐴∩𝐵|
                                   𝑀𝐽𝑆2(𝐴, 𝐵) =                                             (4)
                                                       |B|

Additionally, there are two different applicable weighting methods, respectively IDF-
weighting and T-statistic weighting, to improve Jaccard similarity metric.

B25 measure. If we make a comparison of cited text identification issue with infor-
mation retrieval, citation text may correspond to query, while cited sentence to docu-
ment to be retrieved. Since B25 measure is well-known metric in classical information
retrieval, it may also serve as similarity metric in our work.
   In total, there were 36 similarity metrics (as shown in Table 1), which were used in
the first-stage similarity ranking.

               Table 1. The similarity metrics used in the similarity-based ranking.
    Similarity metrics                   Subclass                          Number of features
    Cosine similarity               TF-IDF based VSM                              2
         metric                    Word2vec based model                           3
                                SVM Kernel transforming model                     3
    Jaccard similarity                   Unigram                                  9
         metric                           Bigram                                  9
                                          Trigram                                 9
         BM25                                                                     1
                                                                                             5


        Total                                                                   36


3.2    Top-K sentence selection based on similarity ranking
Among above 36 similarity metrics, we try to select the best metric as the top-k selec-
tion feature. We used F1.5 measure（β=1.5）to evaluate which similarity metric work
well, since the top K sentence selection result are expected that it involves the more
positive samples.
                                          (𝛽 2 +1)𝑃𝑅
                                   𝐹𝛽 =                                                    (5)
                                            𝛽 2 𝑃+𝑅

   We used the training data of the CL-SciSumm 2017 dataset to evaluate the perfor-
mance of each kind of similarity metric. We found that the 33th feature (T-statistic
weighted tri-gram MJS1 similarity) is best metric for top-K selection in the first stage.
Table 2 shows the performance of the top-k selection based on the 33th similarity met-
ric.

Table 2. The performance of top-K sentence selection based on Jaccard similarity on the train-
                         ing data of CL-SciSumm 2017 datasets.

                 K         Recall          Precision         F1.5-measure
                 1         9.96            14.35             10.99
                 2         16.17           11.65             14.45
                 3         21.79           10.46             16.35
                 4         25.74           9.27              16.64
                 5         29.39           8.47              16.69
                 6         32.94           7.91              16.69
                 7         35.40           7.28              16.18
                 8         36.98           6.66              15.40
From the result like as shown in Table 2, we selected top-5 selection based on T-statistic
weighted tri-gram MJS1 similarity, which was also used as a baseline system.


3.3    Supervised Listwise ranking based on NN model
Recently, deep learning approaches have gradually gained popularity in artificial intel-
ligent problems. However, it is usually applied in classification problem, but not in
ranking problem.
   For ranking, there are three kinds of methods: pointwise, pairwise and listwise. Pair-
wise ranking is almost same effect with classification, in other words, this task is for-
malized as classification of object pairs (correct sample and incorrect sample), such as
RankSVM [4, 7] and RankNet [1]. Pairwise ranking can be referred as multiple classi-
fication, therefore, its performance can be depend on the individual classification, how-
ever, in cited text identification, the performance of the individual classifier is not ideal.
In contrast, listwise ranking approach directly consider overall rank of all samples of
6


the list, but it is needed to annotate the order of all samples. In the cited text identifica-
tion task, however, it is very difficult to annotate all the order of samples.
   Therefore, we proposed a novel listwise ranking model, namely CiteListNet, based
on deep learnings. Given citance 𝑐 and reference sentence list 𝑟1 , . . . , 𝑟𝑛 , the our task
can be formulated as following:
                                                           exp(𝐹(𝑐𝑖 ,𝑟𝑖𝑗))
                𝑆(𝑐𝑖 , 𝑟𝑖𝑗 ) = 𝑆𝑜𝑓𝑡𝑚𝑎𝑥(𝐹(𝑐𝑖 , 𝑟𝑖𝑗 )) =                                    (6)
                                                         ∑𝑗 exp(𝐹(𝑐𝑖 ,𝑟𝑖𝑗))


                         𝑂𝑏𝑒𝑗𝑐𝑡𝑖𝑣𝑒: max ∑𝑖,𝑗 𝑦𝑖𝑗 ∗ 𝑆(𝑐𝑖 , 𝑟𝑖𝑗 )                           (7)

                          1, 𝑟𝑖𝑗 𝑖𝑠 𝑐𝑖𝑡𝑒𝑑 𝑡𝑒𝑥𝑡 𝑜𝑓 𝑎 𝑐𝑖𝑡𝑎𝑖𝑜𝑛 𝑐𝑖
                  𝑦𝑖𝑗 = {                                                                 (8)
                         0, 𝑟𝑖𝑗 𝑖𝑠 𝑛𝑜𝑡 𝑐𝑖𝑡𝑒𝑑 𝑡𝑒𝑥𝑡 𝑜𝑓 𝑎 𝑐𝑖𝑡𝑎𝑡𝑖𝑜𝑛 𝑐𝑖

Where F(.) denotes a deep learning model, which can be implemented as any architec-
ture.
   In above optimization, we only consider the order of positive sample, but not nega-
tive sample. The more the score of positive sample is, the higher the order of it is. If its
score is over 0.5, then its order become the first.
   In addition to similarity-based metric, we also involved the section information in
our feature set. Section information represents in which kind of section the reference
sentence appears in the reference paper. The kind of section is decided based on rule-
based method. Table 3 shows the kinds of section which frequently appear in the com-
putational linguistics field.

                            Table 3. The various kinds of section.
       No          Kind of section                      No           Kind of section
       1           Title                                7            Analysis
       2           Abstract                             8            Experiment
       3           Introduction                         9            Data
       4           Related work                         10           Future work
       5           Method                               11           Other
       6           Conclusion



4      Experiments and Result

The CL-SciSumm 2017 datasets contain 30 topics as training data and 10 topics as test
data [5], where each topic consists of a reference paper and some citing papers that
involves the citation to the reference paper. In this section, we will exploit these datasets
to demonstrate our proposed method.
   To train the list-wise ranking model, we prepared the subset from the training data
through the top-5 sentence selection as described in Section 3.2.
                                                                                                7


   In our experiment, the top-2 listwise ranking showed the best performance on the
training data where its F1-score is observed as 17.9%.
   In order to validate the generalization ability of our method, we try to evaluate the
trained model on the test data in the CL-SciSumm 2017 datasets. The result demon-
strated that the top-2 listwise ranking showed a best performance and that the overfit-
ting did not occur during our listwise ranking training.
   We also compared our method with earlier approaches as shown in Table 4. To eval-
uate the performance of the different systems, two kinds of metric is used: sentence ID
overlap and ROUGE scoring [5, 6, 8]. The former use the raw number of overlapping
sentences between system output and the gold standard to calculate the precision, recall
and F1-score. This evaluation also exploit the micro-average and macro-average re-
spectively. As shown in Table 4, our proposed listwise rank model showed the best
performance over all the kinds of evaluation metric. Furthermore, baseline (MJS-based
top-5 sentence selection) also showed the comparable performance.
   The experiment results demonstrate that our proposed method outperforms any prior
approaches.

  Table 4. The performances of various system on the test data in CL-SciSumm 2017 Shared
                                           Task1.
       System               Micro-Avg (F1)          Macro-Avg (F1)               ROUGE-2 (F1)
       NJUST                     12.3                   14.6                        11.4
      TUGRAZ                     11.0                   13.5                        10.8
        CIST                     10.7                   11.3                         4.7
       NUDT                      14.8                     -                           -
     CiteListNet                 15.3                   18.3                        14.3
      Baseline                   11.7                   12.9                         4.9

   As shown in Table 5, top-2 ranking also showed a good performance on the test data
in CL-SciSumm 19 Shared Task.

  Table 5. The performances of various top-N ranking on the test data in CL-SciSumm 2019
                                     Shared Task [12].
                    Top-N               Micro-Avg (F1)          ROUGE-SU4 (F1)
                      2                      12.4                   9.0
                      3                      11.8                   7.9
                      4                      10.4                   4.1
                 5 (Baseline)                 9.8                   3.0




      1 In Table 4, the result of the prior works is from Jaidka’s report [5].
8


5         Discussion

In this paper, we focused on the cited text identification issue and proposed a novel
method, namely CiteListNet, based on listwise ranking.
    The main contributions of this paper are two points: feature selection and a novel
listwise ranking model.
    First, we adopted new features to identify cited text. We modified Jaccard similarity
to consider how much the cited sentence covers the citation text and how much the
citation text covers the cited text. The former is verified to be useful for identifying
cited text, while the letter is less helpful than other features. When using the Jaccard
similarity, it is recommended to use N-gram language model. Moreover, we found that
T-statistics that represents how much the word is probable to appear in the cited text
could be used as a useful weight. The experimental result shows that T-statistics
weighted Modified Jaccard Similarity feature based on tri-gram language model is the
most useful feature and MJS-based top-5 sentence selection shows the comparable per-
formance, although no training is done.
    Second, we proposed a novel listwise ranking model based on deep learning -
CiteListNet. We found that our proposed method is stable and did not occur overfitting
problem during the training process. The result of our experiment shows that our novel
method outperforms other prior approaches.
    In this paper, it was still not considered that cited text could be represented as a
paragraph. In this case, the relationship between the cited sentence and the citation text
may have a different characteristic. In the future work, we will focus this issue, and
apply this research result to various bibliometric task.

Acknowledgement. This paper is one of the research outputs of the project supported
by the State Key Program of National Social Science Foundation of China (Grant No.
17ATQ001).



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