Abstract
The TAC 2010 summarization track initiated a new task—aspect-guided summarization—that centers on textual aspects embodied as particular kinds of information of a text. We observe that aspect-guided summaries not only address highly specific user need, but also facilitate content-level coherence by using aspect information. In this article, we present a full-fledged approach to aspect-guided summarization with a focus on summary coherence. Our summarization approach depends on two prerequisite subtasks: recognizing aspect-bearing sentences in order to do sentence extraction, and modeling aspect-based coherence with an HMM model in order to predict a coherent sentence ordering. Using the manually annotated TAC 2010 and 2010 datasets, we validated the effectiveness of our proposed methods for those subtasks. Drawing on the empirical results, we proceed to develop an aspect-guided summarizer based on a simple but robust base summarizer. With sentence selection guided by aspect information, our system is one of the best on TAC 2011. With sentence ordering predicted by the aspect-based HMM model, the summaries achieve good coherence.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: ACM Transactions on Speech and Language Processing
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
