TY - GEN
T1 - Extractive summarization based on event term temporal relation graph and critical chain
AU - Liu, Maofu
AU - Li, Wenjie
AU - Hu, Huijun
PY - 2009/12/1
Y1 - 2009/12/1
N2 - In this paper, we investigate whether temporal relations among event terms can help improve event-based extractive summarization and text cohesion of machine-generated summaries. Using the verb semantic relation, namely happens-before provided by VerbOcean, we construct an event term temporal relation graph for source documents. We assume that the maximal weakly connected component on this graph represents the main topic of source documents. The event terms in the temporal critical chain identified from the maximal weakly connected component are then used to calculate the significance of the sentences in source documents. The most significant sentences are included in final summaries. Experiments conducted on the DUC 2001 corpus show that extractive summarization based on event term temporal relation graph and critical chain is able to organize final summaries in a more coherent way and accordingly achieves encouraging improvement over the well-known tf*idf-based and PageRank-based approaches.
AB - In this paper, we investigate whether temporal relations among event terms can help improve event-based extractive summarization and text cohesion of machine-generated summaries. Using the verb semantic relation, namely happens-before provided by VerbOcean, we construct an event term temporal relation graph for source documents. We assume that the maximal weakly connected component on this graph represents the main topic of source documents. The event terms in the temporal critical chain identified from the maximal weakly connected component are then used to calculate the significance of the sentences in source documents. The most significant sentences are included in final summaries. Experiments conducted on the DUC 2001 corpus show that extractive summarization based on event term temporal relation graph and critical chain is able to organize final summaries in a more coherent way and accordingly achieves encouraging improvement over the well-known tf*idf-based and PageRank-based approaches.
UR - http://www.scopus.com/inward/record.url?scp=70549108076&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-04769-5_8
DO - 10.1007/978-3-642-04769-5_8
M3 - Conference article published in proceeding or book
SN - 3642047688
SN - 9783642047688
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 87
EP - 99
BT - Information Retrieval Technology - 5th Asia Information Retrieval Symposium, AIRS 2009, Proceedings
T2 - 5th Asia Information Retrieval Symposium, AIRS 2009
Y2 - 21 October 2009 through 23 October 2009
ER -