[2208.04083] Template-based Abstractive Microblog Opinion Summarisation
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Computer Science > Computation and Language

arXiv:2208.04083 (cs)
[Submitted on 8 Aug 2022 (v1), last revised 3 Oct 2022 (this version, v2)]

Title:Template-based Abstractive Microblog Opinion Summarisation

Authors:Iman Munire Bilal, Bo Wang, Adam Tsakalidis, Dong Nguyen, Rob Procter, Maria Liakata
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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.
Comments: Accepted for publication in Transactions of the Association for Computational Linguistics (TACL), 2022. Pre-MIT Press publication version
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2208.04083 [cs.CL]
  (or arXiv:2208.04083v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2208.04083

Submission history

From: Iman Bilal [view email]
[v1] Mon, 8 Aug 2022 12:16:01 UTC (731 KB)
[v2] Mon, 3 Oct 2022 10:50:55 UTC (724 KB)
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