The decomposition of human-written summary sentences Export

@inproceedings{citeulike:3576179, abstract = {We define the problem of decomposing human-written summary sentences and propose a novel Hidden Markov Model solution to the problem. Human summarizers often rely on cutting and pasting of the full document to generate summaries. Decomposing a human-written summary sentence requires determining: (1) whether it is constructed by cutting and pasting, (2) what components in the sentence come from the original document, and (3) where in the document the components come from. Solving the decomposition problem can potentially lead to the automatic acquisition of large corpora for summarization. It also sheds light on the generation of summary text by cutting and pasting. The evaluation shows that the proposed decomposition algorithm performs well.}, author = {Jing, Hongyan and Mckeown, Kathleen R.}, booktitle = {University of California, Beekely}, citeulike-article-id = {3576179}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.2214}, keywords = {summarisation, summary-alignment, text-mining}, pages = {129--136}, posted-at = {2008-11-19 03:14:16}, priority = {2}, title = {The decomposition of human-written summary sentences}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.2214}, year = {1999} }

TY - CONF ID - citeulike:3576179 L3 - citeulike-article-id:3576179 N2 - We define the problem of decomposing human-written summary sentences and propose a novel Hidden Markov Model solution to the problem. Human summarizers often rely on cutting and pasting of the full document to generate summaries. Decomposing a human-written summary sentence requires determining: (1) whether it is constructed by cutting and pasting, (2) what components in the sentence come from the original document, and (3) where in the document the components come from. Solving the decomposition problem can potentially lead to the automatic acquisition of large corpora for summarization. It also sheds light on the generation of summary text by cutting and pasting. The evaluation shows that the proposed decomposition algorithm performs well. EP - 136 TI - The decomposition of human-written summary sentences T2 - University of California, Beekely SP - 129 KW - summarisation KW - summary-alignment KW - text-mining AU - Jing, Hongyan AU - Mckeown, Kathleen PY - 1999/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.33.2214 ER -