Name
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. |
Year | Venue | Field |
|---|---|---|
2015 | Asia-Pacific Signal and Information Processing Association Annual Summit and Conference | Automatic summarization,Multi-document summarization,Computer science,Maximum likelihood,Speech recognition,Natural language processing,Artificial intelligence,Probabilistic logic,Sentence,Speech summarization,Language model,Salient |
DocType | ISSN | Citations |
Conference | 2309-9402 | 0 |
PageRank | References | Authors |
0.34 | 24 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shih-Hung Liu | 1 | 66 | 14.53 |
| Hung-Shin Lee | 2 | 53 | 9.76 |
| Hsiao-Tsung Hung | 3 | 0 | 0.68 |
| Kuan-Yu Chen | 4 | 450 | 55.78 |
| Berlin Chen | 5 | 151 | 34.59 |
| Hsin-min Wang | 6 | 1201 | 129.62 |
| Hsu-chun Yen | 7 | 595 | 70.67 |
| Wen-Lian Hsu | 8 | 1701 | 198.40 |