Extractive Summarization Using Cohesion Network Analysis and Submodular Set Functions [Konferenzbericht] Paper presented at the International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) (22nd, 2020).

Autor/inn/en	Cioaca, Valentin Sergiu; Dascalu, Mihai; McNamara, Danielle S.
Titel	Extractive Summarization Using Cohesion Network Analysis and Submodular Set Functions [Konferenzbericht] Paper presented at the International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) (22nd, 2020).
Quelle	(2021), (9 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Documentation; Computer Software; Open Source Technology; Algorithms; Connected Discourse; Network Analysis; Graphs; Information Retrieval; English; Romance Languages; Natural Language Processing + Suchen Sie Ihr Suchwort? Dokumentation; Algorithm; Algorithmus; Netzplantechnik; Grafische Darstellung; English language; Englisch; Romanische Sprache; Natürliche Sprache
Abstract	Numerous approaches have been introduced to automate the process of text summarization, but only few can be easily adapted to multiple languages. This paper introduces a multilingual text processing pipeline integrated in the open-source "ReaderBench" framework, which can be retrofit to cover more than 50 languages. While considering the extensibility of the approach and the problem of missing labeled data for training in various languages besides English, an unsupervised algorithm was preferred to perform extractive summarization (i.e., select the most representative sentences from the original document). Specifically, two different approaches relying on text cohesion were implemented:(1) a graph-based text representation derived from Cohesion Network Analysis that extends TextRank; and (2) a class of submodular set functions. Evaluations were performed on the DUC dataset and use as baseline the implementation of TextRank from Gensim. Our results using the submodular set functions outperform the baseline. In addition, two use cases on English and Romanian languages are presented, with corresponding graphical representations for the two methods. [This paper was published in: 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) Proceedings, 2020, pp. 161-168 (ISBN 978-1-7281-7628-4).] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01