Template-based Abstractive Microblog Opinion Summarisation
- URL: http://arxiv.org/abs/2208.04083v1
- Date: Mon, 8 Aug 2022 12:16:01 GMT
- Title: Template-based Abstractive Microblog Opinion Summarisation
- Authors: Iman Munire Bilal, Bo Wang, Adam Tsakalidis, Dong Nguyen, Rob Procter,
Maria Liakata
- Abstract summary: We introduce the task of microblog opinion summarisation (MOS) and share a dataset of 3100 gold-standard opinion summaries.
The dataset contains summaries of tweets spanning a 2-year period and covers more topics than any other public Twitter summarisation dataset.
- Score: 26.777997436856076
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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