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        <title>Template-based Abstractive Microblog Opinion Summarization</title>
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        <namePart type="given">Iman</namePart>
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    <abstract>We introduce the task of microblog opinion summarization (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 summarization dataset. Summaries are abstractive in nature and have been created by journalists skilled in summarizing 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 favors extractive summarization models. To showcase the dataset‘s utility and challenges, we benchmark a range of abstractive and extractive state-of-the-art summarization 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.</abstract>
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%0 Journal Article
%T Template-based Abstractive Microblog Opinion Summarization
%A Bilal, Iman Munire
%A Wang, Bo
%A Tsakalidis, Adam
%A Nguyen, Dong
%A Procter, Rob
%A Liakata, Maria
%J Transactions of the Association for Computational Linguistics
%D 2022
%V 10
%I MIT Press
%C Cambridge, MA
%F bilal-etal-2022-template
%X We introduce the task of microblog opinion summarization (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 summarization dataset. Summaries are abstractive in nature and have been created by journalists skilled in summarizing 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 favors extractive summarization models. To showcase the dataset‘s utility and challenges, we benchmark a range of abstractive and extractive state-of-the-art summarization 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.
%R 10.1162/tacl_a_00516
%U https://aclanthology.org/2022.tacl-1.71/
%U https://doi.org/10.1162/tacl_a_00516
%P 1229-1248

Markdown (Informal)

[Template-based Abstractive Microblog Opinion Summarization](https://aclanthology.org/2022.tacl-1.71/) (Bilal et al., TACL 2022)

• Template-based Abstractive Microblog Opinion Summarization (Bilal et al., TACL 2022)

ACL

• Iman Munire Bilal, Bo Wang, Adam Tsakalidis, Dong Nguyen, Rob Procter, and Maria Liakata. 2022. Template-based Abstractive Microblog Opinion Summarization. Transactions of the Association for Computational Linguistics, 10:1229–1248.