Information gain ratio as term weight: the case of summarization of IR results

Authors:
Tatsunori Mori
Affiliations:
Yokohama National University, Yokohama, Japan
Venue:
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Year:
2002

Citing 5
Cited 6

C4.5: programs for machine learning

C4.5: programs for machine learning
Advantages of query biased summaries in information retrieval

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The use of MMR, diversity-based reranking for reordering documents and producing summaries

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Summarizing Similarities and Differences Among Related Documents

Information Retrieval
Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies

NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLPWorkshop on Automatic summarization - Volume 4

Multi-answer-focused multi-document summarization using a question-answering engine

ACM Transactions on Asian Language Information Processing (TALIP)
Multi-answer-focused multi-document summarization using a question-answering engine

COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Proposing a new term weighting scheme for text categorization

AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Term-weighting for summarization of multi-party spoken dialogues

MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
Text summarisation in progress: a literature review

Artificial Intelligence Review
Machine learning based typology development in archaeology

Journal on Computing and Cultural Heritage (JOCCH)

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a new term weighting method for summarizing documents retrieved by IR system. Unlike query-biased summarization, our method utilizes not the information of query, but the similarity information among original documents by hierarchical clustering. To map the similarity structure of the clusters into the weight of each word, we adopt the information gain ratio of probabilistic distribution of each word as term weight.