Search (5 results, page 1 of 1)

0.014365172 = product of:
  0.028730344 = sum of:
    0.028730344 = product of:
      0.114921376 = sum of:
        0.114921376 = weight(_text_:authors in 3028) [ClassicSimilarity], result of:
          0.114921376 = score(doc=3028,freq=8.0), product of:
            0.22816232 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.050048612 = queryNorm
            0.50368255 = fieldWeight in 3028, product of:
              2.828427 = tf(freq=8.0), with freq of:
                8.0 = termFreq=8.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3028)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

Purpose - Scientific abstracts reproduce only part of the information and the complexity of argumentation in a scientific article. The purpose of this paper provides a first analysis of the similarity between the text of scientific abstracts and the body of articles, using sentences as the basic textual unit. It contributes to the understanding of the structure of abstracts. Design/methodology/approach - Using sentence-based similarity metrics, the authors quantify the phenomenon of text re-use in abstracts and examine the positions of the sentences that are similar to sentences in abstracts in the introduction, methods, results and discussion structure, using a corpus of over 85,000 research articles published in the seven Public Library of Science journals. Findings - The authors provide evidence that 84 percent of abstract have at least one sentence in common with the body of the paper. Studying the distributions of sentences in the body of the articles that are re-used in abstracts, the authors show that there exists a strong relation between the rhetorical structure of articles and the zones that authors re-use when writing abstracts, with sentences mainly coming from the beginning of the introduction and the end of the conclusion. Originality/value - Scientific abstracts contain what is considered by the author(s) as information that best describe documents' content. This is a first study that examines the relation between the contents of abstracts and the rhetorical structure of scientific articles. The work might provide new insight for improving automatic abstracting tools as well as information retrieval approaches, in which text organization and structure are important features.

0.010157711 = product of:
  0.020315422 = sum of:
    0.020315422 = product of:
      0.08126169 = sum of:
        0.08126169 = weight(_text_:authors in 3459) [ClassicSimilarity], result of:
          0.08126169 = score(doc=3459,freq=4.0), product of:
            0.22816232 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.050048612 = queryNorm
            0.35615736 = fieldWeight in 3459, product of:
              2.0 = tf(freq=4.0), with freq of:
                4.0 = termFreq=4.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.0390625 = fieldNorm(doc=3459)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

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.

0.008619104 = product of:
  0.017238207 = sum of:
    0.017238207 = product of:
      0.06895283 = sum of:
        0.06895283 = weight(_text_:authors in 3981) [ClassicSimilarity], result of:
          0.06895283 = score(doc=3981,freq=2.0), product of:
            0.22816232 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.050048612 = queryNorm
            0.30220953 = fieldWeight in 3981, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.046875 = fieldNorm(doc=3981)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

In the interest of standardization and quality assurance, it is desirable for authors and staff of access services to follow the American National Standards Institute (ANSI) guidelines in preparing abstracts. Using the statistical approach an extraction system (the Abstraction Assistant) was developed to generate informative abstracts to meet the ANSI guidelines for structural content elements. The system performance is evaluated by comparing the system-generated abstracts with the author's original abstracts and the manually enhanced system abstracts on three criteria: balance (satisfaction of the ANSI standards), fluency (text coherence), and understandability (clarity). The results suggest that it is possible to use the system output directly without manual modification, but there are issues that need to be addressed in further studies to make the system a better tool.

0.0084761195 = product of:
  0.016952239 = sum of:
    0.016952239 = product of:
      0.033904478 = sum of:
        0.033904478 = weight(_text_:22 in 2640) [ClassicSimilarity], result of:
          0.033904478 = score(doc=2640,freq=2.0), product of:
            0.1752617 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.050048612 = queryNorm
            0.19345059 = fieldWeight in 2640, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=2640)
      0.5 = coord(1/2)
  0.5 = coord(1/2)

Date

22. 1.2016 12:29:41

0.007182586 = product of:
  0.014365172 = sum of:
    0.014365172 = product of:
      0.057460688 = sum of:
        0.057460688 = weight(_text_:authors in 5400) [ClassicSimilarity], result of:
          0.057460688 = score(doc=5400,freq=2.0), product of:
            0.22816232 = queryWeight, product of:
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.050048612 = queryNorm
            0.25184128 = fieldWeight in 5400, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.558814 = idf(docFreq=1258, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5400)
      0.25 = coord(1/4)
  0.5 = coord(1/2)

Abstract

Automatic subject prediction is a desirable feature for modern digital library systems, as manual indexing can no longer cope with the rapid growth of digital collections. It is also desirable to be able to identify a small set of entities (e.g., authors, citations, bibliographic records) which are most relevant to a query. This gets more difficult when the amount of data increases dramatically. Data sparsity and model scalability are the major challenges to solving this type of extreme multilabel classification problem automatically. In this paper, we propose to address this problem in two steps: we first embed different types of entities into the same semantic space, where similarity could be computed easily; second, we propose a novel non-parametric method to identify the most relevant entities in addition to direct semantic similarities. We show how effectively this approach predicts even very specialised subjects, which are associated with few documents in the training set and are more problematic for a classifier.