TY - GEN
T1 - Enhancing sentence ordering by hierarchical topic modeling for multi-document summarization
AU - Yang, Guangbing
AU - Kinshuk,
AU - Wen, Dunwei
AU - Sutinen, Erkki
PY - 2013
Y1 - 2013
N2 - The sentence ordering is a difficult but very important task in multi-document summarization. With the aim of producing a coherent and legible summary for multiple documents, this study proposes a novel approach that is built upon a hierarchical topic model for automatic evaluation of sentence ordering. By learning topic correlations from the topic hierarchies, this model is able to automatically evaluate sentences to find a plausible order to arrange them for generating a more readable summary. The experimental results demonstrate that our proposed approach can improve the summarization performance and present a significant enhancement on the sentence ordering for multi-document summarization. In addition, the experimental results show that our model can automatically analyze the topic relationships to infer a strategy for sentence ordering. Human evaluations justify that the generated summaries, which implement this strategy, demonstrate a good linguistic performance in terms of coherence, readability, and redundancy.
AB - The sentence ordering is a difficult but very important task in multi-document summarization. With the aim of producing a coherent and legible summary for multiple documents, this study proposes a novel approach that is built upon a hierarchical topic model for automatic evaluation of sentence ordering. By learning topic correlations from the topic hierarchies, this model is able to automatically evaluate sentences to find a plausible order to arrange them for generating a more readable summary. The experimental results demonstrate that our proposed approach can improve the summarization performance and present a significant enhancement on the sentence ordering for multi-document summarization. In addition, the experimental results show that our model can automatically analyze the topic relationships to infer a strategy for sentence ordering. Human evaluations justify that the generated summaries, which implement this strategy, demonstrate a good linguistic performance in terms of coherence, readability, and redundancy.
KW - Hierarchical topic model
KW - Machine learning
KW - Sentence ordering
KW - Text summarization
UR - http://www.scopus.com/inward/record.url?scp=84894216214&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-45114-0_30
DO - 10.1007/978-3-642-45114-0_30
M3 - Published Conference contribution
AN - SCOPUS:84894216214
SN - 9783642451133
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 367
EP - 379
BT - Advances in Artificial Intelligence and Its Applications - 12th Mexican International Conference on Artificial Intelligence, MICAI 2013, Proceedings
T2 - 12th Mexican International Conference on Artificial Intelligence, MICAI 2013
Y2 - 24 November 2013 through 30 November 2013
ER -