Template-based Abstractive Microblog Opinion Summarization
Iman Munire Bilal, Bo Wang, Adam Tsakalidis, Dong Nguyen, Rob Procter, Maria Liakata
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.- Anthology ID:
- 2022.tacl-1.71
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 10
- Month:
- Year:
- 2022
- Address:
- Cambridge, MA
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 1229–1248
- Language:
- URL:
- https://aclanthology.org/2022.tacl-1.71
- DOI:
- 10.1162/tacl_a_00516
- Cite (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.
- Cite (Informal):
- Template-based Abstractive Microblog Opinion Summarization (Bilal et al., TACL 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.tacl-1.71.pdf