The use of MMR, diversity-based reranking for reordering documents and producing summaries

The use of MMR, diversity-based reranking for reordering documents and producing summaries (1998)

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by Jaime Carbonell , Jade Goldstein

Venue:	In SIGIR
Citations:	376 - 8 self

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Datum	Value	Source
TITLE	The use of MMR, diversity-based reranking for reordering documents and producing summaries	INFERENCE
AUTHOR NAME	Jaime Carbonell	SVM HeaderParse 0.2
AUTHOR AFFIL	Language Technologies Institute; Carnegie Mellon University	SVM HeaderParse 0.2
AUTHOR NAME	Jade Goldstein	SVM HeaderParse 0.2
AUTHOR AFFIL	Language Technologies Institute; Carnegie Mellon University	SVM HeaderParse 0.2
ABSTRACT	jadeQcs.cmu.edu Abstract This paper presents a method for combining query-relevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in re-ranking retrieved documents and in selecting apprw priate passages for text summarization. Preliminary results indicate some benefits for MMR diversity ranking in document retrieval and in single document summarization. The latter are borne out by the recent results of the SUMMAC conference in the evaluation of summarization systems. However, the clearest advantage is demonstrated in constructing non-redundant multi-document summaries, where MMR results are clearly superior to non-MMR passage selection. 2 Maximal Marginal Relevance Most modem IR search engines produce a ranked list of retrieved documents ordered by declining relevance to the user’s query. In contrast, we motivated the need for “relevant novelty ” as a potentially superior criterion. A first approximation to measuring relevant novelty is to measure relevance and novelty independently and provide a linear combination as the metric. We call the linear combination “marginal relevance ”- i.e. a document has high marginal relevance if it is both relevant to the query and contains minimal similarity to previously selected documents. We strive to maximize-marginal relevance in retrieval and summarization, hence we label our method “maximal marginal relevanci ” (MMR).	SVM HeaderParse 0.2
YEAR	1998	INFERENCE
VENUE	In SIGIR	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	335--336	INFERENCE
CITATIONS	3 found	ParsCit 1.0