Automatic Twitter topic summarization with speech acts

Renxian Zhang, Wenjie Li, Dehong Gao, You Ouyang

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

Abstract

With the growth of the social media service of Twitter, automatic summarization of Twitter messages (tweets) is in urgent need for efficient processing of the massive tweeted information. Unlike multi-document summarization in general, Twitter topic summarization must handle the numerous, short, dissimilar, and noisy nature of tweets. To address this challenge, we propose a novel speech act-guided summarization approach in this work. Speech acts characterize tweeters’ communicative behavior and provide an organized view of their messages. Speech act recognition is a multi-class classification problem, which we solve by using word-based and symbol-based features that capture both the linguistic features of speech acts and the particularities of Twitter text. The recognized speech acts in tweets are then used to direct the extraction of key words and phrases to fill in templates designed for speech acts. Leveraging high-ranking words and phrases as well as topic information for major speech acts, we propose a round-robin algorithm to generate template-based summaries. Different from the extractive method adopted in most previous works, our summarization method is abstractive. Evaluated on two 100-topic datasets, the summaries generated by our method outperform two kinds of representative extractive summaries and rival human-written summaries in terms of explanatoriness and informativeness.

Original languageEnglish
Title of host publicationSocial Media Content Analysis
Subtitle of host publicationNatural Language Processing and Beyond
PublisherWorld Scientific Publishing Co. Pte. Ltd.
Pages347-373
Number of pages27
ISBN (Electronic)9789813223615
ISBN (Print)9789813223608
DOIs
Publication statusPublished - 1 Jan 2017

ASJC Scopus subject areas

  • Computer Science(all)

Cite this