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Template-based abstractive microblog opinion summarisation
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Bilal, Iman Munire, Wang, Bo, Tsakalidis, Adam, Nguyen, Dong, Procter, Rob and Liakata, Maria (2022) Template-based abstractive microblog opinion summarisation. Transactions of the Association for Computational Linguistics, 10 . pp. 1229-1248. doi:10.1162/tacl_a_00516 ISSN 2307-387X.
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Official URL: http://doi.org/10.1162/tacl_a_00516
Abstract
We introduce the task of microblog opinion summarisation (MOS) and share a dataset of 3100 gold-standard opinion summaries to facilitate research in this domain. The dataset contains summaries of tweets spanning a 2-year period and covers more topics than any other public Twitter summarisation dataset. Summaries are abstractive in nature and have been created by journalists skilled in summarising news articles following a template separating factual information (main story) from author opinions. Our method differs from previous work on generating gold-standard summaries from social media, which usually involves selecting representative posts and thus favours extractive summarisation models. To showcase the dataset’s utility and challenges, we benchmark a range of abstractive and extractive state-of-the-art summarisation models and achieve good performance, with the former outperforming the latter. We also show that fine-tuning is necessary to improve performance and investigate the benefits of using different sample sizes.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | |||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
Library of Congress Subject Headings (LCSH): | Automatic abstracting, Computational linguistics, Content analysis (Communication), Natural language processing (Computer science), Data mining, Artificial intelligence -- Computer programs, Social media -- Computer networks | |||||||||
Journal or Publication Title: | Transactions of the Association for Computational Linguistics | |||||||||
Publisher: | Association for Computational Linguistics | |||||||||
ISSN: | 2307-387X | |||||||||
Official Date: | December 2022 | |||||||||
Dates: |
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Volume: | 10 | |||||||||
Page Range: | pp. 1229-1248 | |||||||||
DOI: | 10.1162/tacl_a_00516 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Date of first compliant deposit: | 2 August 2022 | |||||||||
Date of first compliant Open Access: | 15 December 2022 | |||||||||
RIOXX Funder/Project Grant: |
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