Difference between revisions of "Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network"

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'''Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network''' - scientific work related to Wikipedia quality published in 2013, written by Daniil Mirylenka and Andrea Passerini.
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'''Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Daniil Mirylenka]] and [[Andrea Passerini]].
  
 
== Overview ==
 
== Overview ==
Searching for scientific publications on the Web is a tedious task, especially when exploring an unfamiliar domain. Typical scholarly search engines produce lengthy unstructured result lists that are difficult to comprehend, interpret and browse. Authors propose a novel method of organizing the search results into concise and informative topic hierarchies. The method consists of two steps: extracting interrelated topics from the result set, and summarizing the topic graph. In the first step authors map the search results to articles and categories of Wikipedia, constructing a graph of relevant topics with hierarchical relations. In the second step authors sequentially build nested summaries of the produced topic graph using a structured output prediction approach. Trained on a small number of examples, method learns to construct informative summaries for unseen topic graphs, and outperforms unsupervised state-of-the-art Wikipedia-based clustering.
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Searching for scientific publications on the Web is a tedious task, especially when exploring an unfamiliar domain. Typical scholarly search engines produce lengthy unstructured result lists that are difficult to comprehend, interpret and browse. Authors propose a novel method of organizing the search results into concise and informative topic hierarchies. The method consists of two steps: extracting interrelated topics from the result set, and summarizing the topic graph. In the first step authors map the search results to articles and [[categories]] of [[Wikipedia]], constructing a graph of relevant topics with hierarchical relations. In the second step authors sequentially build nested summaries of the produced topic graph using a structured output prediction approach. Trained on a small number of examples, method learns to construct informative summaries for unseen topic graphs, and outperforms unsupervised state-of-the-art Wikipedia-based clustering.

Revision as of 06:54, 10 January 2020

Navigating the Topical Structure of Academic Search Results via the Wikipedia Category Network - scientific work related to Wikipedia quality published in 2013, written by Daniil Mirylenka and Andrea Passerini.

Overview

Searching for scientific publications on the Web is a tedious task, especially when exploring an unfamiliar domain. Typical scholarly search engines produce lengthy unstructured result lists that are difficult to comprehend, interpret and browse. Authors propose a novel method of organizing the search results into concise and informative topic hierarchies. The method consists of two steps: extracting interrelated topics from the result set, and summarizing the topic graph. In the first step authors map the search results to articles and categories of Wikipedia, constructing a graph of relevant topics with hierarchical relations. In the second step authors sequentially build nested summaries of the produced topic graph using a structured output prediction approach. Trained on a small number of examples, method learns to construct informative summaries for unseen topic graphs, and outperforms unsupervised state-of-the-art Wikipedia-based clustering.