@inproceedings{69eaa40f38d4452688445d6555ebe599,
title = "Incorporating proximity information in relevance language modeling for extractive speech summarization",
abstract = "Extractive speech summarization refers to automatic selection of an indicative set of sentences from a spoken document so as to offer a concise digest covering the most salient aspects of the original document. The language modeling (LM) framework alongside the pseudo-relevance feedback (PRF) technique has emerged as a promising line of research for conducting extractive speech summarization in an unsupervised manner, showing some preliminary success. This paper extends such a general line of research and its main contributions are two-fold. First, we explore several effective formulations of proximity-based cues for use in the sentence modeling process involved in the LM-based summarization framework. Second, the utilities of the methods instantiated from the LM-based summarization framework and several well-practiced state-of-the-art methods are analyzed and compared extensively. The empirical results suggest the effectiveness of our methods.",
author = "Liu, \{Shih Hung\} and Lee, \{Hung Shih\} and Hung, \{Hsiao Tsung\} and Chen, \{Kuan Yu\} and Berlin Chen and Wang, \{Hsin Min\} and Yen, \{Hsu Chun\} and Hsu, \{Wen Lian\}",
note = "Publisher Copyright: {\textcopyright} 2015 Asia-Pacific Signal and Information Processing Association.; 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 ; Conference date: 16-12-2015 Through 19-12-2015",
year = "2016",
month = feb,
day = "19",
doi = "10.1109/APSIPA.2015.7415303",
language = "English",
series = "2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "401--407",
booktitle = "2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015",
}