Vilares, J.; Alonso, M.A.; Doval, Y.; Vilares, M.: Studying the effect and treatment of misspelled queries in Cross-Language Information Retrieval (2016)
0.00
0.0019955188 = product of:
0.011973113 = sum of:
0.011973113 = weight(_text_:in in 2974) [ClassicSimilarity], result of:
0.011973113 = score(doc=2974,freq=10.0), product of:
0.059380736 = queryWeight, product of:
1.3602545 = idf(docFreq=30841, maxDocs=44218)
0.043654136 = queryNorm
0.20163295 = fieldWeight in 2974, product of:
3.1622777 = tf(freq=10.0), with freq of:
10.0 = termFreq=10.0
1.3602545 = idf(docFreq=30841, maxDocs=44218)
0.046875 = fieldNorm(doc=2974)
0.16666667 = coord(1/6)
- Abstract
- General graph random walk has been successfully applied in multi-document summarization, but it has some limitations to process documents by this way. In this paper, we propose a novel hypergraph based vertex-reinforced random walk framework for multi-document summarization. The framework first exploits the Hierarchical Dirichlet Process (HDP) topic model to learn a word-topic probability distribution in sentences. Then the hypergraph is used to capture both cluster relationship based on the word-topic probability distribution and pairwise similarity among sentences. Finally, a time-variant random walk algorithm for hypergraphs is developed to rank sentences which ensures sentence diversity by vertex-reinforcement in summaries. Experimental results on the public available dataset demonstrate the effectiveness of our framework.
