Advancing Human-Like Summarization: Approaches to Text Summarization

dc.contributor.authorGowhar, S.
dc.contributor.authorSharma, B.
dc.contributor.authorGupta, A.K.
dc.contributor.authorAnand Kumar, A.K.
dc.date.accessioned2026-02-06T06:34:28Z
dc.date.issued2023
dc.description.abstractText summarization, a well-explored domain within Natural Language Processing, has witnessed significant progress. The ILSUM shared task, encompassing various languages, such as English, Hindi, Gujarati, and Bengali, concentrates on text summarization. The proposed research focuses on leveraging pretrained sequence-to-sequence models for abstractive summarization specifically in the context of the English language. This paper provides an extensive exposition of our model and approach. Notably, we achieved the top ranking in the English Language subtask. Furthermore, this paper dives into an analysis of various techniques for extractive summarization, presenting their outcomes and drawing comparisons with abstractive summarization. © 2023 Copyright for this paper by its authors.
dc.identifier.citationCEUR Workshop Proceedings, 2023, Vol.3681, , p. 747-754
dc.identifier.issn16130073
dc.identifier.urihttps://doi.org/
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/29254
dc.publisherCEUR-WS
dc.subjectAbstractive
dc.subjectExtractive Summarization
dc.subjectSequence-to-Sequence models
dc.subjectText Summarization
dc.titleAdvancing Human-Like Summarization: Approaches to Text Summarization

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