Multi-Document Summarization by Maximizing Informative Content-Words
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1776-1781.
Abstract
We show that a simple procedure based on maximizing the number of informative content-words can produce some of the best reported results for multi-document summarization. We first assign a score to each term in the document cluster, using only frequency and position information, and then we find the set of sentences in the document cluster that maximizes the sum of these scores, subject to length constraints. Our overall results are the best reported on the DUC-2004 summarization task for the ROUGE-1 score, and are the best, but not statistically significantly different from the best system in MSE-2005. Our system is also substantially simpler than the previous best system.
@inproceedings{ijcai2007p287,
title = {Multi-Document Summarization by Maximizing Informative Content-Words},
author = {Yih, Wen-tau and Goodman, Joshua and Vanderwende, Lucy and Suzuki, Hisami},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1776--1781},
year = {2007},
url = {https://ijcai.org/proceedings/2007/287},
}
TY - CPAPER TI - Multi-Document Summarization by Maximizing Informative Content-Words AU - Yih, Wen-tau AU - Goodman, Joshua AU - Vanderwende, Lucy AU - Suzuki, Hisami T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1776 EP - 1781 UR - https://ijcai.org/proceedings/2007/287 ER -Copied
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