Biased LexRank: Passage Retrieval Using Random Walks with Question-based priors
See link. Interesting approach for ranking sentences given a query - like Topic sensitive PageRank.
They focus on query-based or focused summarization: generate a summary of a set of related documents given a specific aspect of their common topic formulated as a natural language query. In generic summarization the objective is to cover as much salient information in the original documents as possible.
More specifically: Given a set of documents about a topic (e.g. "international adoption"), the systems are required to produce a summary that focuses on the given aspect of that topic (such as "what are the laws, problems and issues surrounding international adoption by American families?") . This is referred to as Passage retrieval in information retrieval. In Question Answering, one tries to retrieve a few sentences that are relevant to question and thus potentially contain the answer. However, in summarization, we look for longer answers that are several sentences long.
Their approach: Given a single parameter one can determine how much of the relevant passage should be query-independent or query-based. It is thus semi-supervised. They do not use any particular linguistic resources. They consider intra-sentence similarities in addition to similarity of the candidate sentences to the query.
Their ranking technique of sentences thus includes - relevance and intra-sentence similarity.
They focus on query-based or focused summarization: generate a summary of a set of related documents given a specific aspect of their common topic formulated as a natural language query. In generic summarization the objective is to cover as much salient information in the original documents as possible.
More specifically: Given a set of documents about a topic (e.g. "international adoption"), the systems are required to produce a summary that focuses on the given aspect of that topic (such as "what are the laws, problems and issues surrounding international adoption by American families?") . This is referred to as Passage retrieval in information retrieval. In Question Answering, one tries to retrieve a few sentences that are relevant to question and thus potentially contain the answer. However, in summarization, we look for longer answers that are several sentences long.
Their approach: Given a single parameter one can determine how much of the relevant passage should be query-independent or query-based. It is thus semi-supervised. They do not use any particular linguistic resources. They consider intra-sentence similarities in addition to similarity of the candidate sentences to the query.
Their ranking technique of sentences thus includes - relevance and intra-sentence similarity.
posted by Manish at 7:52 PM
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