Using tweets to help sentence compression for news highlights generation

Zhongyu Wei, Yang Liu, Chen Li, Wei Gao

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

We explore using relevant tweets of a given news article to help sentence compression for generating compressive news highlights. We extend an unsupervised dependency-tree based sentence compression approach by incorporating tweet information to weight the tree edge in terms of informativeness and syntactic importance. The experimental results on a public corpus that contains both news articles and relevant tweets show that our proposed tweets guided sentence compression method can improve the summarization performance significantly compared to the baseline generic sentence compression method.

Original languageEnglish
Title of host publicationSocial Media Content Analysis
Subtitle of host publicationNatural Language Processing and Beyond
PublisherWorld Scientific Publishing Co. Pte Ltd
Pages309-320
Number of pages12
ISBN (Electronic)9789813223615
ISBN (Print)9789813223608
DOIs
Publication statusPublished - 1 Jan 2017

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ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Wei, Z., Liu, Y., Li, C., & Gao, W. (2017). Using tweets to help sentence compression for news highlights generation. In Social Media Content Analysis: Natural Language Processing and Beyond (pp. 309-320). World Scientific Publishing Co. Pte Ltd. https://doi.org/10.1142/9789813223615_0021