C
Chin-Yew Lin
Researcher at Microsoft
Publications - 220
Citations - 22809
Chin-Yew Lin is an academic researcher from Microsoft. The author has contributed to research in topics: Automatic summarization & Question answering. The author has an hindex of 54, co-authored 209 publications receiving 18750 citations. Previous affiliations of Chin-Yew Lin include Peking University & China Center of Advanced Science and Technology.
Papers
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Proceedings Article
ROUGE: A Package for Automatic Evaluation of Summaries
TL;DR: Four different RouGE measures are introduced: ROUGE-N, ROUge-L, R OUGE-W, and ROUAGE-S included in the Rouge summarization evaluation package and their evaluations.
Proceedings ArticleDOI
Automatic evaluation of summaries using N-gram co-occurrence statistics
Chin-Yew Lin,Eduard Hovy +1 more
TL;DR: The results show that automatic evaluation using unigram co-occurrences between summary pairs correlates surprising well with human evaluations, based on various statistical metrics; while direct application of the BLEU evaluation procedure does not always give good results.
Proceedings ArticleDOI
Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics
Chin-Yew Lin,Franz Josef Och +1 more
TL;DR: Two new objective automatic evaluation methods for machine translation based on longest common subsequence between a candidate translation and a set of reference translations and relaxes strict n-gram matching to skip-bigram matching are described.
Proceedings ArticleDOI
The automated acquisition of topic signatures for text summarization
Chin-Yew Lin,Eduard Hovy +1 more
TL;DR: A method for automatically training topic signatures-sets of related words, with associated weights, organized around head topics, is described and illustrated with signatures the authors created with 6,194 TREC collection texts over 4 selected topics.
Proceedings Article
Automated Text Summarization in SUMMARIST
Eduard Hovy,Chin-Yew Lin +1 more
TL;DR: The system’s architecture is described and details of some of its modules, many of them trained on large corpora of text, are provided.