Supervised machine learning for extractive query based summarisation of biomedical data

Mandeep Kaur; Diego Molla

Supervised machine learning for extractive query based summarisation of biomedical data

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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 language	English
Title of host publication	Ninth International Workshop on Health Text Mining and Information Analysis (LOUHI)
Subtitle of host publication	Proceedings of the Workshop
Place of Publication	Stroudsburg
Publisher	Association for Computational Linguistics
Pages	29-37
Number of pages	9
ISBN (Electronic)	9781948087742
Publication status	Published - 2018
Event	2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) - Brussels, Belgium Duration: 31 Oct 2018 → 4 Nov 2018

Conference

Conference	2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Country/Territory	Belgium
City	Brussels
Period	31/10/18 → 4/11/18

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Cite this

@inproceedings{5c7c4034066a4a8192f18e9c1d20db9c,

title = "Supervised machine learning for extractive query based summarisation of biomedical data",

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.",

author = "\{Mandeep Kaur\} and Diego Molla",

year = "2018",

language = "English",

pages = "29--37",

booktitle = "Ninth International Workshop on Health Text Mining and Information Analysis (LOUHI)",

publisher = "Association for Computational Linguistics",

note = "2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) ; Conference date: 31-10-2018 Through 04-11-2018",

}

Mandeep Kaur, & Molla, D 2018, Supervised machine learning for extractive query based summarisation of biomedical data. in Ninth International Workshop on Health Text Mining and Information Analysis (LOUHI): Proceedings of the Workshop. Association for Computational Linguistics, Stroudsburg, pp. 29-37, 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), Brussels, Belgium, 31/10/18. <https://aclanthology.info/papers/W18-5604/w18-5604>

Supervised machine learning for extractive query based summarisation of biomedical data. / Mandeep Kaur, ; Molla, Diego.
Ninth International Workshop on Health Text Mining and Information Analysis (LOUHI): Proceedings of the Workshop. Stroudsburg: Association for Computational Linguistics, 2018. p. 29-37.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AU - Mandeep Kaur, null

AU - Molla, Diego

PY - 2018

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N2 - 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.

AB - 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.

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M3 - Conference proceeding contribution

AN - SCOPUS:85122042251

SP - 29

EP - 37

BT - Ninth International Workshop on Health Text Mining and Information Analysis (LOUHI)

PB - Association for Computational Linguistics

CY - Stroudsburg

T2 - 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Y2 - 31 October 2018 through 4 November 2018

ER -

Supervised machine learning for extractive query based summarisation of biomedical data

Abstract

Conference

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