Search (9 results, page 1 of 1)

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Abstract

Multidocument summarization (MDS) aims for each given query to extract compressed and relevant information with respect to the different query-related themes present in a set of documents. Many approaches operate in two steps. Themes are first identified from the set, and then a summary is formed by extracting salient sentences within the different documents of each of the identified themes. Among these approaches, latent semantic analysis (LSA) based approaches rely on spectral decomposition techniques to identify the themes. In this article, we propose a major extension of these techniques that relies on the quantum information access (QIA) framework. The latter is a framework developed for modeling information access based on the probabilistic formalism of quantum physics. The QIA framework not only points out the limitations of the current LSA-based approaches, but motivates a new principled criterium to tackle multidocument summarization that addresses these limitations. As a byproduct, it also provides a way to enhance the LSA-based approaches. Extensive experiments on the DUC 2005, 2006 and 2007 datasets show that the proposed approach consistently improves over both the LSA-based approaches and the systems that competed in the yearly DUC competitions. This demonstrates the potential impact of quantum-inspired approaches to information access in general, and of the QIA framework in particular.

Source

Journal of the American Society for Information Science and Technology. 63(2012) no.5, S.865-888

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Abstract

Summarisation is traditionally used to produce summaries of the textual contents of documents. In this paper, it is argued that summarisation methods can also be applied to the logical structure of XML documents. Structure summarisation selects the most important elements of the logical structure and ensures that the user's attention is focused towards sections, subsections, etc. that are believed to be of particular interest. Structure summaries are shown to users as hierarchical tables of contents. This paper discusses methods for structure summarisation that use various features of XML elements in order to select document portions that a user's attention should be focused to. An evaluation methodology for structure summarisation is also introduced and summarisation results using various summariser versions are presented and compared to one another. We show that data sets used in information retrieval evaluation can be used effectively in order to produce high quality (query independent) structure summaries. We also discuss the choice and effectiveness of particular summariser features with respect to several evaluation measures.

Content

Beitrag in einem Themenheft "Large-Scale and Distributed Systems for Information Retrieval" Vgl.: doi:10.1016/j.ipm.2011.11.002.

Source

Information processing and management. 48(2012) no.5, S.956-968

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Abstract

Our consumption of online information is mediated by filtering, ranking, and recommendation algorithms that introduce unintentional biases as they attempt to deliver relevant and engaging content. It has been suggested that our reliance on online technologies such as search engines and social media may limit exposure to diverse points of view and make us vulnerable to manipulation by disinformation. In this article, we mine a massive data set of web traffic to quantify two kinds of bias: (i) homogeneity bias, which is the tendency to consume content from a narrow set of information sources, and (ii) popularity bias, which is the selective exposure to content from top sites. Our analysis reveals different bias levels across several widely used web platforms. Search exposes users to a diverse set of sources, while social media traffic tends to exhibit high popularity and homogeneity bias. When we focus our analysis on traffic to news sites, we find higher levels of popularity bias, with smaller differences across applications. Overall, our results quantify the extent to which our choices of online systems confine us inside "social bubbles."

Source

Journal of the Association for Information Science and Technology. 70(2019) no.3, S.218-229

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Source

Journal of the American Society for Information Science and Technology. 63(2012) no.12, S.2463-2473

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Source

Journal of the Association for Information Science and Technology. 65(2014) no.1, S.129-145

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Source

Journal of the Association for Information Science and Technology. 65(2014) no.10, S.1988-2005

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Source

Journal of the Association for Information Science and Technology. 68(2017) no.4, S.869-883

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Source

Journal of the Association for Information Science and Technology. 68(2017) no.5, S.1149-1164

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Source

Journal of the Association for Information Science and Technology. 69(2018) no.7, S.913-923