A Lightweight Constrained Generation Alternative for Query-focused Summarization
Abstract
Supplementary Material
- Download
- 18.92 MB
References
Index Terms
- A Lightweight Constrained Generation Alternative for Query-focused Summarization
Recommendations
Sentiment-oriented query-focused text summarization addressed with a multi-objective optimization approach
AbstractNowadays, the automatic text summarization is a highly relevant task in many contexts. In particular, query-focused summarization consists of generating a summary from one or multiple documents according to a query given by the user. ...
Highlights- The sentiment-oriented query-focused text summarization problem is tackled.
- ...
Query-focused multi-document summarization based on query-sensitive feature space
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge managementQuery-oriented relevance, information richness and novelty are important requirements in query-focused summarization, which, to a considerable extent, determine the summary quality. Previous work either rarely took into account all above demands ...
Using query expansion in graph-based approach for query-focused multi-document summarization
This paper presents a novel query expansion method, which is combined in the graph-based algorithm for query-focused multi-document summarization, so as to resolve the problem of information limit in the original query. Our approach makes use of both ...
Comments
Information & Contributors
Information
Published In
![cover image ACM Conferences](/cms/asset/4e8f66bc-75d3-45c7-a334-53feea7b06e2/3539618.cover.jpg)
- General Chairs:
- Hsin-Hsi Chen,
- Wei-Jou (Edward) Duh,
- Hen-Hsen Huang,
- Program Chairs:
- Makoto P. Kato,
- Josiane Mothe,
- Barbara Poblete
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Check for updates
Author Tags
Qualifiers
- Short-paper
Funding Sources
- Advancing Theoretical Minimax Deep Learning: Optimization, Resilience, and Interpretability.
- SCH: Geometry and Topology for Interpretable and Reliable Deep Learning in Medical Imaging
Conference
Acceptance Rates
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 362Total Downloads
- Downloads (Last 12 months)362
- Downloads (Last 6 weeks)30
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in