Template-based Abstractive Microblog Opinion Summarization

IF 4.2 1区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Transactions of the Association for Computational Linguistics Pub Date : 2022-08-08 DOI:10.1162/tacl_a_00516

I. Bilal, Bo Wang, A. Tsakalidis, Dong Nguyen, R. Procter, M. Liakata

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引用次数: 3

Abstract

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.

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基于模板的微博意见摘要

摘要本文引入了微博意见摘要的任务，并分享了3100个金标准意见摘要的数据集，以促进该领域的研究。该数据集包含2年期间的tweet摘要，涵盖的主题比任何其他公共Twitter摘要数据集都多。摘要本质上是抽象的，是由熟练的记者根据将事实信息(主要故事)与作者观点分开的模板总结新闻文章而创作的。我们的方法不同于之前从社交媒体生成金标准摘要的工作，后者通常涉及选择具有代表性的帖子，因此更倾向于提取摘要模型。为了展示数据集的实用性和挑战，我们对一系列抽象和提取的最先进的总结模型进行了基准测试，并取得了良好的性能，前者的表现优于后者。我们还表明，微调是必要的，以提高性能和调查使用不同的样本大小的好处。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Transactions of the Association for Computational Linguistics Multiple-

CiteScore

32.60

自引率

4.60%

发文量

审稿时长

8 weeks

期刊介绍： The highly regarded quarterly journal Computational Linguistics has a companion journal called Transactions of the Association for Computational Linguistics. This open access journal publishes articles in all areas of natural language processing and is an important resource for academic and industry computational linguists, natural language processing experts, artificial intelligence and machine learning investigators, cognitive scientists, speech specialists, as well as linguists and philosophers. The journal disseminates work of vital relevance to these professionals on an annual basis.