Paper Info
Title
Positional Language Modeling For Extractive Broadcast News Speech Summarization
Abstract
Extractive summarization, with the intention of automatically selecting a set of representative sentences from a text (or spoken) document so as to concisely express the most important theme of the document, has been an active area of experimentation and development. A recent trend of research is to employ the language modeling (LM) approach for important sentence selection, which has proven to be effective for performing extractive summarization in an unsupervised fashion. However, one of the major challenges facing the LM approach is how to formulate the sentence models and estimate their parameters more accurately for each text (or spoken) document to be summarized. This paper extends this line of research and its contributions are three-fold. First, we propose a positional language modeling framework using different granularities of position-specific information to better estimate the sentence models involved in summarization. Second, we also explore to integrate the positional cues into relevance modeling through a pseudo-relevance feedback procedure. Third, the utilities of the various methods originated from our proposed framework and several well established unsupervised methods are analyzed and compared extensively. Empirical evaluations conducted on a broadcast news summarization task seem to demonstrate the performance merits of our summarization methods.
Year
Venue
Keywords
2015
16TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2015), VOLS 1-5
extractive broadcast news summarization, positional language modeling, relevance modeling
Field
DocType
Citations 
Broadcasting,Multi-document summarization,Automatic summarization,Computer science,Speech recognition,Natural language processing,Artificial intelligence,Sentence,Language model,Speech summarization
Conference
4
PageRank 
References 
Authors
0.41
22
6
Name
Order
Citations
PageRank
Shih-Hung Liu16614.53
Kuan-Yu Chen245055.78
Berlin Chen315134.59
Hsin-min Wang41201129.62
Hsu-chun Yen559570.67
Wen-Lian Hsu61701198.40