Difference between revisions of "Using Wikipedia Anchor Text and Weighted Clustering Coefficient to Enhance the Traditional Multi-Document Summarization"
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| + | | title = Using Wikipedia Anchor Text and Weighted Clustering Coefficient to Enhance the Traditional Multi-Document Summarization | ||
| + | | date = 2012 | ||
| + | | authors = [[Niraj Kumar]]<br />[[Kannan Srinathan]]<br />[[Vasudeva Varma]] | ||
| + | | doi = 10.1007/978-3-642-28601-8_33 | ||
| + | | link = https://dl.acm.org/citation.cfm?id=2238735 | ||
| + | }} | ||
'''Using Wikipedia Anchor Text and Weighted Clustering Coefficient to Enhance the Traditional Multi-Document Summarization''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Niraj Kumar]], [[Kannan Srinathan]] and [[Vasudeva Varma]]. | '''Using Wikipedia Anchor Text and Weighted Clustering Coefficient to Enhance the Traditional Multi-Document Summarization''' - scientific work related to [[Wikipedia quality]] published in 2012, written by [[Niraj Kumar]], [[Kannan Srinathan]] and [[Vasudeva Varma]]. | ||
== Overview == | == Overview == | ||
Similar to the traditional approach, authors consider the task of summarization as selection of top ranked sentences from ranked sentence-clusters. To achieve this goal, authors rank the sentence clusters by using the importance of words calculated by using page rank algorithm on reverse directed word graph of sentences. Next, to rank the sentences in every cluster authors introduce the use of weighted clustering coefficient. Authors use page rank score of words for calculation of weighted clustering coefficient. Finally the most important issue is the presence of a lot of noisy entries in the text, which downgrades the performance of most of the text mining algorithms. To solve this problem, authors introduce the use of [[Wikipedia]] anchor text based phrase mapping scheme. Authors experimental results on DUC-2002 and DUC-2004 dataset show that system performs better than unsupervised systems and better than/comparable with novel supervised systems of this area. | Similar to the traditional approach, authors consider the task of summarization as selection of top ranked sentences from ranked sentence-clusters. To achieve this goal, authors rank the sentence clusters by using the importance of words calculated by using page rank algorithm on reverse directed word graph of sentences. Next, to rank the sentences in every cluster authors introduce the use of weighted clustering coefficient. Authors use page rank score of words for calculation of weighted clustering coefficient. Finally the most important issue is the presence of a lot of noisy entries in the text, which downgrades the performance of most of the text mining algorithms. To solve this problem, authors introduce the use of [[Wikipedia]] anchor text based phrase mapping scheme. Authors experimental results on DUC-2002 and DUC-2004 dataset show that system performs better than unsupervised systems and better than/comparable with novel supervised systems of this area. | ||
Revision as of 13:11, 11 January 2020
Authors | Niraj Kumar Kannan Srinathan Vasudeva Varma |
|---|---|
Publication date | 2012 |
DOI | 10.1007/978-3-642-28601-8_33 |
Links | Original |
Using Wikipedia Anchor Text and Weighted Clustering Coefficient to Enhance the Traditional Multi-Document Summarization - scientific work related to Wikipedia quality published in 2012, written by Niraj Kumar, Kannan Srinathan and Vasudeva Varma.
Overview
Similar to the traditional approach, authors consider the task of summarization as selection of top ranked sentences from ranked sentence-clusters. To achieve this goal, authors rank the sentence clusters by using the importance of words calculated by using page rank algorithm on reverse directed word graph of sentences. Next, to rank the sentences in every cluster authors introduce the use of weighted clustering coefficient. Authors use page rank score of words for calculation of weighted clustering coefficient. Finally the most important issue is the presence of a lot of noisy entries in the text, which downgrades the performance of most of the text mining algorithms. To solve this problem, authors introduce the use of Wikipedia anchor text based phrase mapping scheme. Authors experimental results on DUC-2002 and DUC-2004 dataset show that system performs better than unsupervised systems and better than/comparable with novel supervised systems of this area.
