Paper: Detection of Topic and its Extrinsic Evaluation Through Multi-Document Summarization

ACL ID P14-2040
Title Detection of Topic and its Extrinsic Evaluation Through Multi-Document Summarization
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

This paper presents a method for detect- ing words related to a topic (we call them topic words) over time in the stream of documents. Topic words are widely dis- tributed in the stream of documents, and sometimes they frequently appear in the documents, and sometimes not. We pro- pose a method to reinforce topic words with low frequencies by collecting docu- ments from the corpus, and applied Latent Dirichlet Allocation (Blei et al., 2003) to these documents. For the results of LDA, we identified topic words by using Mov- ing Average Convergence Divergence. In order to evaluate the method, we applied the results of topic detection to extractive multi-document summarization. The re- sults showed that the method was effective for sentence selection in summarization.