Search (4 results, page 1 of 1)

  • × author_ss:"Li, W."
  • × year_i:[2010 TO 2020}
  1. Ouyang, Y.; Li, W.; Li, S.; Lu, Q.: Intertopic information mining for query-based summarization (2010) 0.00 
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    Abstract
    In this article, the authors address the problem of sentence ranking in summarization. Although most existing summarization approaches are concerned with the information embodied in a particular topic (including a set of documents and an associated query) for sentence ranking, they propose a novel ranking approach that incorporates intertopic information mining. Intertopic information, in contrast to intratopic information, is able to reveal pairwise topic relationships and thus can be considered as the bridge across different topics. In this article, the intertopic information is used for transferring word importance learned from known topics to unknown topics under a learning-based summarization framework. To mine this information, the authors model the topic relationship by clustering all the words in both known and unknown topics according to various kinds of word conceptual labels, which indicate the roles of the words in the topic. Based on the mined relationships, we develop a probabilistic model using manually generated summaries provided for known topics to predict ranking scores for sentences in unknown topics. A series of experiments have been conducted on the Document Understanding Conference (DUC) 2006 data set. The evaluation results show that intertopic information is indeed effective for sentence ranking and the resultant summarization system performs comparably well to the best-performing DUC participating systems on the same data set.
    Type
    a
  2. Wei, F.; Li, W.; Liu, S.: iRANK: a rank-learn-combine framework for unsupervised ensemble ranking (2010) 0.00 
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    Abstract
    The authors address the problem of unsupervised ensemble ranking. Traditional approaches either combine multiple ranking criteria into a unified representation to obtain an overall ranking score or to utilize certain rank fusion or aggregation techniques to combine the ranking results. Beyond the aforementioned combine-then-rank and rank-then-combine approaches, the authors propose a novel rank-learn-combine ranking framework, called Interactive Ranking (iRANK), which allows two base rankers to teach each other before combination during the ranking process by providing their own ranking results as feedback to the others to boost the ranking performance. This mutual ranking refinement process continues until the two base rankers cannot learn from each other any more. The overall performance is improved by the enhancement of the base rankers through the mutual learning mechanism. The authors further design two ranking refinement strategies to efficiently and effectively use the feedback based on reasonable assumptions and rational analysis. Although iRANK is applicable to many applications, as a case study, they apply this framework to the sentence ranking problem in query-focused summarization and evaluate its effectiveness on the DUC 2005 and 2006 data sets. The results are encouraging with consistent and promising improvements.
    Type
    a
  3. Cai, X.; Li, W.: Enhancing sentence-level clustering with integrated and interactive frameworks for theme-based summarization (2011) 0.00 
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    Abstract
    Sentence clustering plays a pivotal role in theme-based summarization, which discovers topic themes defined as the clusters of highly related sentences to avoid redundancy and cover more diverse information. As the length of sentences is short and the content it contains is limited, the bag-of-words cosine similarity traditionally used for document clustering is no longer suitable. Special treatment for measuring sentence similarity is necessary. In this article, we study the sentence-level clustering problem. After exploiting concept- and context-enriched sentence vector representations, we develop two co-clustering frameworks to enhance sentence-level clustering for theme-based summarization-integrated clustering and interactive clustering-both allowing word and document to play an explicit role in sentence clustering as independent text objects rather than using word or concept as features of a sentence in a document set. In each framework, we experiment with two-level co-clustering (i.e., sentence-word co-clustering or sentence-document co-clustering) and three-level co-clustering (i.e., document-sentence-word co-clustering). Compared against concept- and context-oriented sentence-representation reformation, co-clustering shows a clear advantage in both intrinsic clustering quality evaluation and extrinsic summarization evaluation conducted on the Document Understanding Conferences (DUC) datasets.
    Type
    a
  4. Liu, Y.; Li, W.; Huang, Z.; Fang, Q.: ¬A fast method based on multiple clustering for name disambiguation in bibliographic citations (2015) 0.00 
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    Abstract
    Name ambiguity in the context of bibliographic citation affects the quality of services in digital libraries. Previous methods are not widely applied in practice because of their high computational complexity and their strong dependency on excessive attributes, such as institutional affiliation, research area, address, etc., which are difficult to obtain in practice. To solve this problem, we propose a novel coarse-to-fine framework for name disambiguation which sequentially employs 3 common and easily accessible attributes (i.e., coauthor name, article title, and publication venue). Our proposed framework is based on multiple clustering and consists of 3 steps: (a) clustering articles by coauthorship and obtaining rough clusters, that is fragments; (b) clustering fragments obtained in step 1 by title information and getting bigger fragments; (c) and clustering fragments obtained in step 2 by the latent relations among venues. Experimental results on a Digital Bibliography and Library Project (DBLP) data set show that our method outperforms the existing state-of-the-art methods by 2.4% to 22.7% on the average pairwise F1 score and is 10 to 100 times faster in terms of execution time.
    Type
    a

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