Supervised machine learning for extractive query based summarisation of biomedical data

Mandeep Kaur, Diego Molla

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

The automation of text summarisation of biomedical publications is a pressing need due to the plethora of information available online. This paper explores the impact of several supervised machine learning approaches for extracting multi-document summaries for given queries. In particular, we compare classification and regression approaches for query-based extractive summarisation using data provided by the BioASQ Challenge. We tackled the problem of annotating sentences
for training classification systems and show that a simple annotation approach outperforms regression-based summarisation.
Original languageEnglish
Title of host publicationNinth International Workshop on Health Text Mining and Information Analysis (LOUHI)
Subtitle of host publicationProceedings of the Workshop
Place of PublicationStroudsburg
PublisherAssociation for Computational Linguistics
Pages29-37
Number of pages9
ISBN (Electronic)9781948087742
Publication statusPublished - 2018
Event2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) - Brussels, Belgium
Duration: 31 Oct 20184 Nov 2018

Conference

Conference2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)
CountryBelgium
CityBrussels
Period31/10/184/11/18

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