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.
|Title of host publication||Social Media Content Analysis|
|Subtitle of host publication||Natural Language Processing and Beyond|
|Publisher||World Scientific Publishing Co. Pte Ltd|
|Number of pages||12|
|Publication status||Published - 1 Jan 2017|
ASJC Scopus subject areas
- Computer Science(all)