Search (12 results, page 1 of 1)

  • × theme_ss:"Automatisches Abstracting"
  1. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.03 
    0.031095807 = product of:
  0.093287416 = sum of:
    0.061100297 = weight(_text_:relationship in 1012) [ClassicSimilarity], result of:
      0.061100297 = score(doc=1012,freq=2.0), product of:
        0.2292412 = queryWeight, product of:
          4.824759 = idf(docFreq=964, maxDocs=44218)
          0.047513504 = queryNorm
        0.26653278 = fieldWeight in 1012, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.824759 = idf(docFreq=964, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1012)
    0.03218712 = weight(_text_:22 in 1012) [ClassicSimilarity], result of:
      0.03218712 = score(doc=1012,freq=2.0), product of:
        0.16638419 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.047513504 = queryNorm
        0.19345059 = fieldWeight in 1012, 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=1012)
  0.33333334 = coord(2/6)
    Abstract
    With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end-to-end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro-avgs of , , and on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models.
    Date
    22. 6.2023 14:55:20
  2. Xiong, S.; Ji, D.: Query-focused multi-document summarization using hypergraph-based ranking (2016) 0.01 
    0.014256737 = product of:
  0.08554042 = sum of:
    0.08554042 = weight(_text_:relationship in 2972) [ClassicSimilarity], result of:
      0.08554042 = score(doc=2972,freq=2.0), product of:
        0.2292412 = queryWeight, product of:
          4.824759 = idf(docFreq=964, maxDocs=44218)
          0.047513504 = queryNorm
        0.3731459 = fieldWeight in 2972, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.824759 = idf(docFreq=964, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2972)
  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.
  3. Martinez-Romo, J.; Araujo, L.; Fernandez, A.D.: SemGraph : extracting keyphrases following a novel semantic graph-based approach (2016) 0.01 
    0.01222006 = product of:
  0.07332036 = sum of:
    0.07332036 = weight(_text_:relationship in 2832) [ClassicSimilarity], result of:
      0.07332036 = score(doc=2832,freq=2.0), product of:
        0.2292412 = queryWeight, product of:
          4.824759 = idf(docFreq=964, maxDocs=44218)
          0.047513504 = queryNorm
        0.31983936 = fieldWeight in 2832, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.824759 = idf(docFreq=964, maxDocs=44218)
          0.046875 = fieldNorm(doc=2832)
  0.16666667 = coord(1/6)
    Abstract
    Keyphrases represent the main topics a text is about. In this article, we introduce SemGraph, an unsupervised algorithm for extracting keyphrases from a collection of texts based on a semantic relationship graph. The main novelty of this algorithm is its ability to identify semantic relationships between words whose presence is statistically significant. Our method constructs a co-occurrence graph in which words appearing in the same document are linked, provided their presence in the collection is statistically significant with respect to a null model. Furthermore, the graph obtained is enriched with information from WordNet. We have used the most recent and standardized benchmark to evaluate the system ability to detect the keyphrases that are part of the text. The result is a method that achieves an improvement of 5.3% and 7.28% in F measure over the two labeled sets of keyphrases used in the evaluation of SemEval-2010.
  4. Yeh, J.-Y.; Ke, H.-R.; Yang, W.-P.; Meng, I.-H.: Text summarization using a trainable summarizer and latent semantic analysis (2005) 0.01 
    0.010183383 = product of:
  0.061100297 = sum of:
    0.061100297 = weight(_text_:relationship in 1003) [ClassicSimilarity], result of:
      0.061100297 = score(doc=1003,freq=2.0), product of:
        0.2292412 = queryWeight, product of:
          4.824759 = idf(docFreq=964, maxDocs=44218)
          0.047513504 = queryNorm
        0.26653278 = fieldWeight in 1003, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.824759 = idf(docFreq=964, maxDocs=44218)
          0.0390625 = fieldNorm(doc=1003)
  0.16666667 = coord(1/6)
    Abstract
    This paper proposes two approaches to address text summarization: modified corpus-based approach (MCBA) and LSA-based T.R.M. approach (LSA + T.R.M.). The first is a trainable summarizer, which takes into account several features, including position, positive keyword, negative keyword, centrality, and the resemblance to the title, to generate summaries. Two new ideas are exploited: (1) sentence positions are ranked to emphasize the significances of different sentence positions, and (2) the score function is trained by the genetic algorithm (GA) to obtain a suitable combination of feature weights. The second uses latent semantic analysis (LSA) to derive the semantic matrix of a document or a corpus and uses semantic sentence representation to construct a semantic text relationship map. We evaluate LSA + T.R.M. both with single documents and at the corpus level to investigate the competence of LSA in text summarization. The two novel approaches were measured at several compression rates on a data corpus composed of 100 political articles. When the compression rate was 30%, an average f-measure of 49% for MCBA, 52% for MCBA + GA, 44% and 40% for LSA + T.R.M. in single-document and corpus level were achieved respectively.
  5. Ouyang, Y.; Li, W.; Li, S.; Lu, Q.: Intertopic information mining for query-based summarization (2010) 0.01 
    0.010183383 = product of:
  0.061100297 = sum of:
    0.061100297 = weight(_text_:relationship in 3459) [ClassicSimilarity], result of:
      0.061100297 = score(doc=3459,freq=2.0), product of:
        0.2292412 = queryWeight, product of:
          4.824759 = idf(docFreq=964, maxDocs=44218)
          0.047513504 = queryNorm
        0.26653278 = fieldWeight in 3459, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          4.824759 = idf(docFreq=964, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3459)
  0.16666667 = coord(1/6)
    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.
  6. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.01 
    0.008583232 = product of:
  0.05149939 = sum of:
    0.05149939 = weight(_text_:22 in 6599) [ClassicSimilarity], result of:
      0.05149939 = score(doc=6599,freq=2.0), product of:
        0.16638419 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.047513504 = queryNorm
        0.30952093 = fieldWeight in 6599, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.0625 = fieldNorm(doc=6599)
  0.16666667 = coord(1/6)
    Date
    26. 2.1997 10:22:43
  7. Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996) 0.01 
    0.008583232 = product of:
  0.05149939 = sum of:
    0.05149939 = weight(_text_:22 in 6751) [ClassicSimilarity], result of:
      0.05149939 = score(doc=6751,freq=2.0), product of:
        0.16638419 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.047513504 = queryNorm
        0.30952093 = fieldWeight in 6751, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.0625 = fieldNorm(doc=6751)
  0.16666667 = coord(1/6)
    Date
    6. 3.1997 16:22:15
  8. Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996) 0.01 
    0.008583232 = product of:
  0.05149939 = sum of:
    0.05149939 = weight(_text_:22 in 6974) [ClassicSimilarity], result of:
      0.05149939 = score(doc=6974,freq=2.0), product of:
        0.16638419 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.047513504 = queryNorm
        0.30952093 = fieldWeight in 6974, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.0625 = fieldNorm(doc=6974)
  0.16666667 = coord(1/6)
    Source
    Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
  9. Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007) 0.01 
    0.0064374236 = product of:
  0.03862454 = sum of:
    0.03862454 = weight(_text_:22 in 948) [ClassicSimilarity], result of:
      0.03862454 = score(doc=948,freq=2.0), product of:
        0.16638419 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.047513504 = queryNorm
        0.23214069 = fieldWeight in 948, 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=948)
  0.16666667 = coord(1/6)
    Abstract
    In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words. This paper details each of these components, together with experiments designed to quantify their individual contributions. We include an analysis of our results on two large datasets commonly used to evaluate task-focused summarization, the DUC2005 and DUC2006 datasets, using automatic metrics. Additionally, we include an analysis of our results on the DUC2006 task according to human evaluation metrics. In the human evaluation of system summaries compared to human summaries, i.e., the Pyramid method, our system ranked first out of 22 systems in terms of overall mean Pyramid score; and in the human evaluation of summary responsiveness to the topic, our system ranked third out of 35 systems.
  10. Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006) 0.01 
    0.00536452 = product of:
  0.03218712 = sum of:
    0.03218712 = weight(_text_:22 in 5290) [ClassicSimilarity], result of:
      0.03218712 = score(doc=5290,freq=2.0), product of:
        0.16638419 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.047513504 = queryNorm
        0.19345059 = fieldWeight in 5290, 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=5290)
  0.16666667 = coord(1/6)
    Date
    22. 7.2006 17:25:48
  11. Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016) 0.01 
    0.00536452 = product of:
  0.03218712 = sum of:
    0.03218712 = weight(_text_:22 in 2640) [ClassicSimilarity], result of:
      0.03218712 = score(doc=2640,freq=2.0), product of:
        0.16638419 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.047513504 = 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)
  0.16666667 = coord(1/6)
    Date
    22. 1.2016 12:29:41
  12. Oh, H.; Nam, S.; Zhu, Y.: Structured abstract summarization of scientific articles : summarization using full-text section information (2023) 0.01 
    0.00536452 = product of:
  0.03218712 = sum of:
    0.03218712 = weight(_text_:22 in 889) [ClassicSimilarity], result of:
      0.03218712 = score(doc=889,freq=2.0), product of:
        0.16638419 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.047513504 = queryNorm
        0.19345059 = fieldWeight in 889, 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=889)
  0.16666667 = coord(1/6)
    Date
    22. 1.2023 18:57:12

Authors

Years