Search (62 results, page 1 of 4)

  • × theme_ss:"Automatisches Abstracting"
  1. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.06 
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    Abstract
    With the onset of the information explosion arising from digital libraries and access to a wealth of information through the Internet, the need to efficiently determine the relevance of a document becomes even more urgent. Describes a text extraction system (TES), which retrieves a set of sentences from a document to form an indicative abstract. Such an automated process enables information to be filtered more quickly. Discusses the combination of various text extraction techniques. Compares results with manually produced abstracts
    Date
    26. 2.1997 10:22:43
  2. Abdi, A.; Idris, N.; Alguliev, R.M.; Aliguliyev, R.M.: Automatic summarization assessment through a combination of semantic and syntactic information for intelligent educational systems (2015) 0.04 
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    Abstract
    Summary writing is a process for creating a short version of a source text. It can be used as a measure of understanding. As grading students' summaries is a very time-consuming task, computer-assisted assessment can help teachers perform the grading more effectively. Several techniques, such as BLEU, ROUGE, N-gram co-occurrence, Latent Semantic Analysis (LSA), LSA_Ngram and LSA_ERB, have been proposed to support the automatic assessment of students' summaries. Since these techniques are more suitable for long texts, their performance is not satisfactory for the evaluation of short summaries. This paper proposes a specialized method that works well in assessing short summaries. Our proposed method integrates the semantic relations between words, and their syntactic composition. As a result, the proposed method is able to obtain high accuracy and improve the performance compared with the current techniques. Experiments have displayed that it is to be preferred over the existing techniques. A summary evaluation system based on the proposed method has also been developed.
    Source
    Information processing and management. 51(2015) no.4, S.340-358
  3. Salton, G.: Automatic text structuring and summarization (1997) 0.04 
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    Abstract
    Applies the ideas from the automatic link generation research to automatic text summarisation. Using techniques for inter-document link generation, generates intra-document links between passages of a document. Based on the intra-document linkage pattern of a text, characterises the structure of the text. Applies the knowledge of text structure to do automatic text summarisation by passage extraction. Evaluates a set of 50 summaries generated using these techniques by comparing the to paragraph extracts constructed by humans. The automatic summarisation methods perform well, especially in view of the fact that the summaries generates by 2 humans for the same article are surprisingly dissimilar
    Source
    Information processing and management. 33(1997) no.2, S.193-207
  4. Summarising software for publishing (1996) 0.04 
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    Abstract
    Reviews 4 software packages designed to provide accurate and indicative summaries of documents by taking the documents and creating distinctive abstracts from them. The products reviewed are: Oracle's ConText; InText's Object Analyzer; Iconovex's AnchorPage; and Software Scientific's Interrogator. Techniques used by the products include: the use of dictionaries of known words and phrases to interpret documents; and heuristic analysis involving weighting all the words in the document solely on their occurrence and position within the document
    Source
    Digital publisher. 1(1996) no.4, S.15-20
  5. Zajic, D.; Dorr, B.J.; Lin, J.; Schwartz, R.: Multi-candidate reduction : sentence compression as a tool for document summarization tasks (2007) 0.03 
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    Abstract
    This article examines the application of two single-document sentence compression techniques to the problem of multi-document summarization-a "parse-and-trim" approach and a statistical noisy-channel approach. We introduce the multi-candidate reduction (MCR) framework for multi-document summarization, in which many compressed candidates are generated for each source sentence. These candidates are then selected for inclusion in the final summary based on a combination of static and dynamic features. Evaluations demonstrate that sentence compression is a valuable component of a larger multi-document summarization framework.
    Source
    Information processing and management. 43(2007) no.6, S.1549-1570
  6. Moens, M.-F.: Summarizing court decisions (2007) 0.03 
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    Abstract
    In the field of law there is an absolute need for summarizing the texts of court decisions in order to make the content of the cases easily accessible for legal professionals. During the SALOMON and MOSAIC projects we investigated the summarization and retrieval of legal cases. This article presents some of the main findings while integrating the research results of experiments on legal document summarization by other research groups. In addition, we propose novel avenues of research for automatic text summarization, which we currently exploit when summarizing court decisions in the ACILA project. Techniques for automated concept learning and argument recognition are here the most challenging.
    Source
    Information processing and management. 43(2007) no.6, S.1748-1764
  7. Nomoto, T.: Discriminative sentence compression with conditional random fields (2007) 0.03 
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    Abstract
    The paper focuses on a particular approach to automatic sentence compression which makes use of a discriminative sequence classifier known as Conditional Random Fields (CRF). We devise several features for CRF that allow it to incorporate information on nonlinear relations among words. Along with that, we address the issue of data paucity by collecting data from RSS feeds available on the Internet, and turning them into training data for use with CRF, drawing on techniques from biology and information retrieval. We also discuss a recursive application of CRF on the syntactic structure of a sentence as a way of improving the readability of the compression it generates. Experiments found that our approach works reasonably well compared to the state-of-the-art system [Knight, K., & Marcu, D. (2002). Summarization beyond sentence extraction: A probabilistic approach to sentence compression. Artificial Intelligence 139, 91-107.].
    Source
    Information processing and management. 43(2007) no.6, S.1571-1587
  8. Shen, D.; Yang, Q.; Chen, Z.: Noise reduction through summarization for Web-page classification (2007) 0.03 
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    Abstract
    Due to a large variety of noisy information embedded in Web pages, Web-page classification is much more difficult than pure-text classification. In this paper, we propose to improve the Web-page classification performance by removing the noise through summarization techniques. We first give empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web-page classification algorithms. We then put forward a new Web-page summarization algorithm based on Web-page layout and evaluate it along with several other state-of-the-art text summarization algorithms on the LookSmart Web directory. Experimental results show that the classification algorithms (NB or SVM) augmented by any summarization approach can achieve an improvement by more than 5.0% as compared to pure-text-based classification algorithms. We further introduce an ensemble method to combine the different summarization algorithms. The ensemble summarization method achieves more than 12.0% improvement over pure-text based methods.
    Source
    Information processing and management. 43(2007) no.6, S.1735-1747
  9. Díaz, A.; Gervás, P.: User-model based personalized summarization (2007) 0.02 
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    Abstract
    The potential of summary personalization is high, because a summary that would be useless to decide the relevance of a document if summarized in a generic manner, may be useful if the right sentences are selected that match the user interest. In this paper we defend the use of a personalized summarization facility to maximize the density of relevance of selections sent by a personalized information system to a given user. The personalization is applied to the digital newspaper domain and it used a user-model that stores long and short term interests using four reference systems: sections, categories, keywords and feedback terms. On the other side, it is crucial to measure how much information is lost during the summarization process, and how this information loss may affect the ability of the user to judge the relevance of a given document. The results obtained in two personalization systems show that personalized summaries perform better than generic and generic-personalized summaries in terms of identifying documents that satisfy user preferences. We also considered a user-centred direct evaluation that showed a high level of user satisfaction with the summaries.
    Source
    Information processing and management. 43(2007) no.6, S.1715-1734
  10. Galgani, F.; Compton, P.; Hoffmann, A.: Summarization based on bi-directional citation analysis (2015) 0.02 
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    Abstract
    Automatic document summarization using citations is based on summarizing what others explicitly say about the document, by extracting a summary from text around the citations (citances). While this technique works quite well for summarizing the impact of scientific articles, other genres of documents as well as other types of summaries require different approaches. In this paper, we introduce a new family of methods that we developed for legal documents summarization to generate catchphrases for legal cases (where catchphrases are a form of legal summary). Our methods use both incoming and outgoing citations, and we show how citances can be combined with other elements of cited and citing documents, including the full text of the target document, and catchphrases of cited and citing cases. On a legal summarization corpus, our methods outperform competitive baselines. The combination of full text sentences and catchphrases from cited and citing cases is particularly successful. We also apply and evaluate the methods on scientific paper summarization, where they perform at the level of state-of-the-art techniques. Our family of citation-based summarization methods is powerful and flexible enough to target successfully a range of different domains and summarization tasks.
    Source
    Information processing and management. 51(2015) no.1, S.1-24
  11. Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007) 0.02 
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    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.
    Source
    Information processing and management. 43(2007) no.6, S.1606-1618
  12. Craven, T.C.: Presentation of repeated phrases in a computer-assisted abstracting tool kit (2001) 0.01 
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    Source
    Information processing and management. 37(2001) no.2, S.221-230
  13. Bateman, J.; Teich, E.: Selective information presentation in an integrated publication system : an application of genre-driven text generation (1995) 0.01 
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    Source
    Information processing and management. 31(1995) no.5, S.753-767
  14. Endres-Niggemeyer, B.: SimSum : an empirically founded simulation of summarizing (2000) 0.01 
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    Source
    Information processing and management. 36(2000) no.4, S.659-682
  15. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.01 
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    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
  16. Moens, M.-F.; Uyttendaele, C.; Dumotier, J.: Abstracting of legal cases : the potential of clustering based on the selection of representative objects (1999) 0.01 
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    Abstract
    The SALOMON project automatically summarizes Belgian criminal cases in order to improve access to the large number of existing and future court decisions. SALOMON extracts text units from the case text to form a case summary. Such a case summary facilitates the rapid determination of the relevance of the case or may be employed in text search. an important part of the research concerns the development of techniques for automatic recognition of representative text paragraphs (or sentences) in texts of unrestricted domains. these techniques are employed to eliminate redundant material in the case texts, and to identify informative text paragraphs which are relevant to include in the case summary. An evaluation of a test set of 700 criminal cases demonstrates that the algorithms have an application potential for automatic indexing, abstracting, and text linkage
  17. Johnson, F.C.; Paice, C.D.; Black, W.J.; Neal, A.P.: ¬The application of linguistic processing to automatic abstract generation (1993) 0.01 
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  18. McKeown, K.; Robin, J.; Kukich, K.: Generating concise natural language summaries (1995) 0.01 
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    Source
    Information processing and management. 31(1995) no.5, S.703-733
  19. Endres-Niggemeyer, B.; Maier, E.; Sigel, A.: How to implement a naturalistic model of abstracting : four core working steps of an expert abstractor (1995) 0.01 
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    Abstract
    4 working steps taken from a comprehensive empirical model of expert abstracting are studied in order to prepare an explorative implementation of a simulation model. It aims at explaining the knowledge processing activities during professional summarizing. Following the case-based and holistic strategy of qualitative empirical research, the main features of the simulation system were developed by investigating in detail a small but central test case - 4 working steps where an expert abstractor discovers what the paper is about and drafts the topic sentence of the abstract
    Source
    Information processing and management. 31(1995) no.5, S.631-674
  20. Dorr, B.J.; Gaasterland, T.: Exploiting aspectual features and connecting words for summarization-inspired temporal-relation extraction (2007) 0.01 
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    Abstract
    This paper presents a model that incorporates contemporary theories of tense and aspect and develops a new framework for extracting temporal relations between two sentence-internal events, given their tense, aspect, and a temporal connecting word relating the two events. A linguistic constraint on event combination has been implemented to detect incorrect parser analyses and potentially apply syntactic reanalysis or semantic reinterpretation - in preparation for subsequent processing for multi-document summarization. An important contribution of this work is the extension of two different existing theoretical frameworks - Hornstein's 1990 theory of tense analysis and Allen's 1984 theory on event ordering - and the combination of both into a unified system for representing and constraining combinations of different event types (points, closed intervals, and open-ended intervals). We show that our theoretical results have been verified in a large-scale corpus analysis. The framework is designed to inform a temporally motivated sentence-ordering module in an implemented multi-document summarization system.
    Source
    Information processing and management. 43(2007) no.6, S.1681-1704

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