Automatically Determining a Proper Length for Multi-Document Summarization: A Bayesian Nonparametric Approach

Anthology ID:: D13-1069
Volume:: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing
Month:: October
Year:: 2013
Address:: Seattle, Washington, USA
Editors:: David Yarowsky, Timothy Baldwin, Anna Korhonen, Karen Livescu, Steven Bethard
Venue:: EMNLP
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 736–746
Language:
URL:: https://aclanthology.org/D13-1069
DOI:
Bibkey:
Cite (ACL):: Tengfei Ma and Hiroshi Nakagawa. 2013. Automatically Determining a Proper Length for Multi-Document Summarization: A Bayesian Nonparametric Approach. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 736–746, Seattle, Washington, USA. Association for Computational Linguistics.
Cite (Informal):: Automatically Determining a Proper Length for Multi-Document Summarization: A Bayesian Nonparametric Approach (Ma & Nakagawa, EMNLP 2013)
Copy Citation:
PDF:: https://preview.aclanthology.org/improve-issue-templates/D13-1069.pdf