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

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.

Automatic Home Medical Product Recommendation

“…In the past, studies have shown that searchers usually prefer diverse search results [19,20]. Nevertheless, existing search result diversification methods [19,20] are designed for the case of a single query.…”

“…Nevertheless, existing search result diversification methods [19,20] are designed for the case of a single query. They cannot be directly applied to our HMP recommendation scenario that uses multiple query phrases.…”

Navigation Interface for Recommending Home Medical Products

“…Third, different child categories can have similarities because their corresponding search guide phrases can link to the same HNA, nursing intervention, or nursing diagnosis. The user would prefer to see dissimilar child categories, especially at the top of the child category list, to quickly gain as much new information as possible [8,16].…”

Select, Link and Rank: Diversified Query Expansion and Entity Ranking Using Wikipedia

“…However, search results would most likely be dominated by pages relating the programming language 3 , that being the dominant interpretation (aka aspect) in the web. Search Result Diversification (SRD) [5,29] refers to the task of selecting and/or re-ranking search results so that many aspects of the query are covered in the top results; this would ensure that the zoologist and comedy-fan in our example are not disappointed with the results. If the British group is to be covered among the top results in a re-ranking based SRD approach for our example, the approach should consider documents that are as deep in the un-diversified ranked list as the rank of the first result that relates to the group.…”

“…In existing work, the extension terms have been identified from sources such as corpus documents [26], query logs [17], external ontologies [2,3] or the results of the initial query [26]. The aspect-affinity of each term is modeled either explicitly [26,17] or implicitly [2] followed by selection of a subset of candidate words using the Maximum Marginal Relevance (MMR) principle [5]. This ensures that terms related to many aspects find a place in the extended set.…”