=Paper=
{{Paper
|id=Vol-3370/keynote1
|storemode=property
|title=Structured Summarisation of News at Scale
|pdfUrl=https://ceur-ws.org/Vol-3370/keynote1.pdf
|volume=Vol-3370
|authors=Georgiana Ifrim
|dblpUrl=https://dblp.org/rec/conf/ecir/Ifrim23
}}
==Structured Summarisation of News at Scale==
<pdf width="1500px">https://ceur-ws.org/Vol-3370/keynote1.pdf</pdf>
<pre>
Structured Summarisation of News at Scale
Georgiana Ifrim
University College Dublin, Ireland




Abstract
Facilitating news consumption at scale is still quite challenging. Some research effort focused on
coming up with useful structures for facilitating news navigation for humans, but benchmarks
and objective evaluation of such structures is not common. One area that has progressed
recently is news timeline summarisation. In this talk, we present some of our work on long-
range large-scale news timeline summarisation. Timelines present the most important events
of a topic linearly in chronological order and are commonly used by news editors to organise
long-ranging topics for news consumers. Tools for automatic timeline summarisation can
address the cost of manual effort and the infeasibility of manually covering many topics, over
long time periods and massive news corpora. In this talk, we first compare different high-level
approaches to timeline summarisation, identify the modules and features important for this
task, and present new state-of-the-art results with a simple new method. We provide several
examples of automatic timelines and present both a quantitative and qualitative analysis of
these structured news summaries. Most of our tools and datasets are available online on github.


Short Bio
Dr. Georgiana Ifrim is an Associate Professor at the School of Computer Science, UCD, co-
lead of the SFI Centre for Research Training in Machine Learning (ML-Labs) and SFI Funded
Investigator with the Insight Centre for Data Analytics and VistaMilk SFI Centre. Dr. Ifrim
holds a PhD and MSc in Machine Learning, from Max-Planck Institute for Informatics, Germany,
and a BSc in Computer Science, from University of Bucharest, Romania. Her research focuses
on effective approaches for large-scale sequence learning, time series classification, and text
mining. She has published more than 50 peer-reviewed articles in top-ranked international
journals and conferences and regularly holds senior positions in the program committees for
IJCAI, AAAI, and ECML-PKDD, as well as being a member of the editorial board of the Machine
Learning Journal, Springer.


In: R. Campos, A. Jorge, A. Jatowt, S. Bhatia, M. Litvak (eds.): Proceedings of the Text2Story’23 Workshop, Dublin
(Republic of Ireland), 2-April-2023
� georgiana.ifrim@ucd.ie (G. Ifrim)
� https://people.ucd.ie/georgiana.ifrim (G. Ifrim)
� 0000-0002-8400-2972 (G. Ifrim)
                                    © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
 CEUR
 Workshop
 Proceedings
               http://ceur-ws.org
               ISSN 1613-0073
                                    CEUR Workshop Proceedings (CEUR-WS.org)




                                                                                                        1

</pre>