| model,4-1-H05-1032,ak | The paper presents a <term> Bayesian model </term> for <term> text summarization </term> , which explicitly encodes and exploits information on how <term> human judgments </term> are distributed over the <term> text </term> . | #5367 The paper presents aBayesian model for text summarization, which explicitly encodes and exploits information on how human judgments are distributed over the text. | |
| tech,7-1-H05-1032,ak | The paper presents a <term> Bayesian model </term> for <term> text summarization </term> , which explicitly encodes and exploits information on how <term> human judgments </term> are distributed over the <term> text </term> . | #5370 The paper presents a Bayesian model fortext summarization, which explicitly encodes and exploits information on how human judgments are distributed over the text. | |
| other,18-1-H05-1032,ak | The paper presents a <term> Bayesian model </term> for <term> text summarization </term> , which explicitly encodes and exploits information on how <term> human judgments </term> are distributed over the <term> text </term> . | #5381 The paper presents a Bayesian model for text summarization, which explicitly encodes and exploits information on howhuman judgments are distributed over the text. | |
| other,24-1-H05-1032,ak | The paper presents a <term> Bayesian model </term> for <term> text summarization </term> , which explicitly encodes and exploits information on how <term> human judgments </term> are distributed over the <term> text </term> . | #5387 The paper presents a Bayesian model for text summarization, which explicitly encodes and exploits information on how human judgments are distributed over thetext. | |
| tech,4-2-H05-1032,ak | Comparison is made against <term> non Bayesian summarizers </term> , using <term> test data </term> from <term> Japanese news texts </term> . | #5393 Comparison is made againstnon Bayesian summarizers, using test data from Japanese news texts. | |
| other,9-2-H05-1032,ak | Comparison is made against <term> non Bayesian summarizers </term> , using <term> test data </term> from <term> Japanese news texts </term> . | #5398 Comparison is made against non Bayesian summarizers, usingtest data from Japanese news texts. | |
| other,12-2-H05-1032,ak | Comparison is made against <term> non Bayesian summarizers </term> , using <term> test data </term> from <term> Japanese news texts </term> . | #5401 Comparison is made against non Bayesian summarizers, using test data fromJapanese news texts. | |
| tech,5-3-H05-1032,ak | It is found that the <term> Bayesian approach </term> generally leverages performance of a <term> summarizer </term> , at times giving it a significant lead over <term> non-Bayesian models </term> . | #5410 It is found that theBayesian approach generally leverages performance of a summarizer, at times giving it a significant lead over non-Bayesian models. | |
| tech,12-3-H05-1032,ak | It is found that the <term> Bayesian approach </term> generally leverages performance of a <term> summarizer </term> , at times giving it a significant lead over <term> non-Bayesian models </term> . | #5417 It is found that the Bayesian approach generally leverages performance of asummarizer, at times giving it a significant lead over non-Bayesian models. | |
| tech,22-3-H05-1032,ak | It is found that the <term> Bayesian approach </term> generally leverages performance of a <term> summarizer </term> , at times giving it a significant lead over <term> non-Bayesian models </term> . | #5427 It is found that the Bayesian approach generally leverages performance of a summarizer, at times giving it a significant lead overnon-Bayesian models. |
