Search (2 results, page 1 of 1)

  • × author_ss:"Xiao, J."
  1. Wan, X.; Yang, J.; Xiao, J.: Incorporating cross-document relationships between sentences for single document summarizations (2006) 0.01 
    0.010370068 = product of:
  0.02592517 = sum of:
    0.007078358 = weight(_text_:a in 2421) [ClassicSimilarity], result of:
      0.007078358 = score(doc=2421,freq=6.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.13239266 = fieldWeight in 2421, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046875 = fieldNorm(doc=2421)
    0.018846812 = product of:
      0.037693623 = sum of:
        0.037693623 = weight(_text_:22 in 2421) [ClassicSimilarity], result of:
          0.037693623 = score(doc=2421,freq=2.0), product of:
            0.16237405 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046368346 = queryNorm
            0.23214069 = fieldWeight in 2421, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=2421)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    Graph-based ranking algorithms have recently been proposed for single document summarizations and such algorithms evaluate the importance of a sentence by making use of the relationships between sentences in the document in a recursive way. In this paper, we investigate using other related or relevant documents to improve summarization of one single document based on the graph-based ranking algorithm. In addition to the within-document relationships between sentences in the specified document, the cross-document relationships between sentences in different documents are also taken into account in the proposed approach. We evaluate the performance of the proposed approach on DUC 2002 data with the ROUGE metric and results demonstrate that the cross-document relationships between sentences in different but related documents can significantly improve the performance of single document summarization.
    Source
    Research and advanced technology for digital libraries : 10th European conference, proceedings / ECDL 2006, Alicante, Spain, September 17 - 22, 2006
    Type
    a
  2. Wan, X.; Yang, J.; Xiao, J.: Towards a unified approach to document similarity search using manifold-ranking of blocks (2008) 0.01 
    0.0066757645 = product of:
  0.01668941 = sum of:
    0.0127425 = weight(_text_:a in 2081) [ClassicSimilarity], result of:
      0.0127425 = score(doc=2081,freq=28.0), product of:
        0.053464882 = queryWeight, product of:
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.046368346 = queryNorm
        0.23833402 = fieldWeight in 2081, product of:
          5.2915025 = tf(freq=28.0), with freq of:
            28.0 = termFreq=28.0
          1.153047 = idf(docFreq=37942, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2081)
    0.003946911 = product of:
      0.007893822 = sum of:
        0.007893822 = weight(_text_:information in 2081) [ClassicSimilarity], result of:
          0.007893822 = score(doc=2081,freq=2.0), product of:
            0.08139861 = queryWeight, product of:
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.046368346 = queryNorm
            0.09697737 = fieldWeight in 2081, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              1.7554779 = idf(docFreq=20772, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2081)
      0.5 = coord(1/2)
  0.4 = coord(2/5)
    Abstract
    Document similarity search (i.e. query by example) aims to retrieve a ranked list of documents similar to a query document in a text corpus or on the Web. Most existing approaches to similarity search first compute the pairwise similarity score between each document and the query using a retrieval function or similarity measure (e.g. Cosine), and then rank the documents by the similarity scores. In this paper, we propose a novel retrieval approach based on manifold-ranking of document blocks (i.e. a block of coherent text about a subtopic) to re-rank a small set of documents initially retrieved by some existing retrieval function. The proposed approach can make full use of the intrinsic global manifold structure of the document blocks by propagating the ranking scores between the blocks on a weighted graph. First, the TextTiling algorithm and the VIPS algorithm are respectively employed to segment text documents and web pages into blocks. Then, each block is assigned with a ranking score by the manifold-ranking algorithm. Lastly, a document gets its final ranking score by fusing the scores of its blocks. Experimental results on the TDT data and the ODP data demonstrate that the proposed approach can significantly improve the retrieval performances over baseline approaches. Document block is validated to be a better unit than the whole document in the manifold-ranking process.
    Source
    Information processing and management. 44(2008) no.3, S.1032-1048
    Type
    a

Authors

Languages

Themes