logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Summarization Based on Task-Oriented Discourse Parsing

delete2015-08-01
delete20
PRE
AI
王珣 cover
王珣 (Xun Wang) *
Y
Yasuhisa Yoshida
X
Xun Wang
K
Katsuhito Sudoh
M
Masaaki Nagata
DOI：10.1109/TASLP.2015.2432573delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Previous research demonstrates that discourse relations can help generate high-quality summaries. Existing studies usually adopt existing discourse parsers directly with no modifications, hence cannot take full advantage of discourse parsing. This paper describes a new single document summarization system. In contrast to previous work, we train a discourse parser specially for summarization by using summaries. The training data are dynamically changed during the training phase to enable the parser to grab the text units that are important for summaries. A special tree-based summary extraction algorithm is designed to work with the new parser. The proposed system enables us to combine discourse parsing and summarization in a unified scheme. Experiments on both the RST-DT and DUC2001 datasets show the effectiveness of the proposed method.
Keywords:
Discourse parsing
discourse relations
summarization
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

I
IEEE-ACM Transactions on Audio Speech and Language Processing
IF:
5.1
Papers:
2.6K
Citations:
1.1W

Organization

No organization information available