Biased LexRank: Passage retrieval using random walks with question-based priors

Otterbacher, Jahna; Erkan, Güneş; Radev, Dragomir

doi:10.1016/j.ipm.2008.06.004

Cited by 84 publications

(58 citation statements)

References 17 publications

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“…Biased LexRank: Biased LexRank [6] provides for a LexRank extension that takes into account a prior document probability distribution, e.g., the relevance of documents to a given query. The Biased LexRank scoring formula is analogous to LexRank scoring formula Equation (10), with matrix A, which represents the probability of jumping to a random node in the graph, proportional to the query document relevance.…”

Section: Algorithmmentioning

confidence: 99%

“…In this work, extending our previous work presented in [2], we extended the methods utilized by incorporating methods introduced for text summarization (LexRank [5] and Biased LexRank [6]) and graph-based ranking (DivRank [7] and Grasshopper [8]) in parallel with the search result diversification methods utilized in our previous work (MMR, Max-sum, Max-min and Mono-objective). We adopted the text summarization and graph-based ranking methods in our diversification schema, and additionally, we utilized various features of our legal dataset.…”

Section: Legal Text Retrievalmentioning

confidence: 99%

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Evaluation of Diversification Techniques for Legal Information Retrieval

2017

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"Public legal information from all countries and international institutions is part of the common heritage of humanity. Maximizing access to this information promotes justice and the rule of law". In accordance with the aforementioned declaration on free access to law by legal information institutes of the world, a plethora of legal information is available through the Internet, while the provision of legal information has never before been easier. Given that law is accessed by a much wider group of people, the majority of whom are not legally trained or qualified, diversification techniques should be employed in the context of legal information retrieval, as to increase user satisfaction. We address the diversification of results in legal search by adopting several state of the art methods from the web search, network analysis and text summarization domains. We provide an exhaustive evaluation of the methods, using a standard dataset from the common law domain that we objectively annotated with relevance judgments for this purpose. Our results: (i) reveal that users receive broader insights across the results they get from a legal information retrieval system; (ii) demonstrate that web search diversification techniques outperform other approaches (e.g., summarization-based, graph-based methods) in the context of legal diversification; and (iii) offer balance boundaries between reinforcing relevant documents or sampling the information space around the legal query.

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Section: Algorithmmentioning

confidence: 99%

Section: Legal Text Retrievalmentioning

confidence: 99%

Evaluation of Diversification Techniques for Legal Information Retrieval

2017

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“…Graph methods have been successfully applied to weighting sentences for generic (Wan and Yang, 2008;Mihalcea and Tarau, 2004;Erkan and Radev, 2004) and query-focused summarization (Otterbacher et al, 2009).…”

Section: Markov Random Walk Model (Mrw)mentioning

confidence: 99%

Improving the Estimation of Word Importance for News Multi-Document Summarization

Hong¹,

Nenkova²

2014

Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

105

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“…Among state of the art methods for this task, graph-based methods that iteratively estimate centrality using linear algebraic methods that converge to a fixed point vector that represents the centrality of every sentence have been widely adopted [11]. When graph-based techniques are applied for query-based summarization, the sentences are also somehow weighted (or selected) based on their similarity to the query [25]. The application of ideas from graph-based summarization to long queries is fairly direct, simply substituting term centrality for sentence centrality.…”

Section: How Does Doppler Ultrasound Take Advantage Of Thementioning

confidence: 99%

A Fixed-Point Method for Weighting Terms in Verbose Informational Queries

Paik

Oard

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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The term weighting and document ranking functions used with informational queries are typically optimized for cases in which queries are short and documents are long. It is reasonable to assume that the presence of a term in a short query reflects some aspect of the topic that is important to the user, and thus rewarding documents that contain the greatest number of distinct query terms is a useful heuristic. Verbose informational queries, such as those that result from cut-and-paste of example text, or that might result from informal spoken interaction, pose a different challenge in which many extraneous (and thus potentially misleading) terms may be present in the query. Modest improvements have been reported from applying supervised methods to learn which terms in a verbose query deserve the greatest emphasis. This paper proposes a novel unsupervised method for weighting terms in verbose informational queries that relies instead on iteratively estimating which terms are most central to the query. The key idea is to use an initial set of retrieval results to define a recursion on the term weight vector that converges to a fixed point representing the vector that optimally describes the initial result set. Experiments with several TREC news and Web test collections indicate that the proposed method often statistically significantly outperforms state of the art supervised methods.

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Biased LexRank: Passage retrieval using random walks with question-based priors

Cited by 84 publications

References 17 publications

Evaluation of Diversification Techniques for Legal Information Retrieval

Evaluation of Diversification Techniques for Legal Information Retrieval

Improving the Estimation of Word Importance for News Multi-Document Summarization

A Fixed-Point Method for Weighting Terms in Verbose Informational Queries

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