A Connectionist Approach to the Generation of Abstracts

Hasida, Kôiti; Ishizaki, Shun; Isahara, Hitoshi

doi:10.1007/978-94-009-3645-4_11

Cited by 10 publications

(3 citation statements)

References 5 publications

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“…Spreading activation is carried out in this network to assess the importance of the elements. Spreading activation has been applied to summarization of single GDA-tagged documents (Hasida et al, 1987;Naga.o and Hasida, 1998). The main conjecture of the present study is that the merit of spreading activation in that it evaluates importances of semantic entities is greater in summarization of multiple documents with multiple topics, because smnmarization techniques using docnment structures do not; apply here, as mentioned em:lier.…”

Section: Multi-document Summarization 31 Spreading Activationmentioning

confidence: 99%

Multi-topic multi-document summarization

Utiyama¹,

Hasida²

2000

Proceedings of the 18th Conference on Computational Linguistics -

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Summarization of multiple documents featuring multiple topics is discussed. The example trea.ted here consists of fifty articles about the Peru hostage incident tbr ])ecember 1996 through April 1997. They include a. lot of topics such as opening, negotiation, ending, and so on. The method proposed in this paper is based on spreading activation over documents syntactically and semantically annotated with GI)A (Global l)ocument Annotation) tags. The method extracts important documents aald important parts therein, and creates a network consisting of important entities and relations among them. It also identifies cross-document coreferences to replace expressions with more concrete ones. The method is essentially multi~ lingua] due to the language-independence of the GDA tagset. This tagset can provide a standard fornm.t tbr the study on the transfbrmation and/or generation stage of summarization process, among other natural language processing tasks.

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Section: Multi-document Summarization 31 Spreading Activationmentioning

confidence: 99%

Multi-topic multi-document summarization

Utiyama¹,

Hasida²

2000

Proceedings of the 18th Conference on Computational Linguistics -

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“…We propose a new summarization method based on GDA. This method employs a spreading activation technique (Hasida et al, 1987) to calculate the importance values of elements in the text. Since the method does not employ any heuristics dependent on the domain and style of documents, it is applicable to any GDA-tagged documents.…”

Section: Text Summarizationmentioning

confidence: 99%

Automatic text summarization based on the Global Document Annotation

Nagao

Hasida²

1998

Proceedings of the 17th International Conference on Computational Linguistics -

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The GDA (Global Document Annotation) project proposes a tag set which allows machines to automatically infer the underlying semantic/pragmatic structure of documents. Its objectives are to promote development and spread of NLP/AI applications to render GDA-tagged documents versatile and intelligent contents, which should nmtivate WWW (World Wide Web) users to tag their documents as part of content authoring. This paper discusses automatic text summarization based on GDA. Its main features are a domain/style-free algorithm and personalization on summarization which reflects readers' interests and preferences. In order to calculate the importance score of a text element, the algorithm uses spreading activation on an intradocument network which connects text elements via thematic, rhetorical, mid coreferential relations. The proposed method is flexible enough to dynamically generate summaries of various sizes. A summary browser supporting personalization is reported as well.

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“…Dell (1986) has developed a psycholinguistic model using spreading activation for selecting candidate items, but his model deals mainly with effects at the level of morphology and phonology. Hasida, Ishizaki, and Isahara (1986) use a spreading activation mechanism to select important information for generating abstracts. We view these three areas of research as complementing our model.…”

Section: Implementation and Coveragementioning

confidence: 99%

Sequencing in a connectionist model of language processing

Gasser

Dyer

1988

Proceedings of the 12th Conference on Computational Linguistics -

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Ret~ ~nt research suggests that human language processing can be profitably viewed in terms of the spread of activation through a network of simple processing units. Decision making in connectionist models such as these is distributed anti consists in selections made from sets of mutually inhibiting candidate items which are activated on the basis of input features. In these models, however, there is the problem, espocially for generation, of obtaining sequential behavior from an essentially parallel process. The thrust of this paper is tlmt sequencing can also be modelled as a process of competition between candidates activated on the basis of input features. In the case of sequencing, the competition concerns which of a set of phrase constituents will appear in a particular output position. This account allows outpu,: ordering to arise out of the interaction of syntactic with semantic anti pragmatic factors, as seems to be the case for human language generation. The paper describes a localized connectionist model of language generation, focusing on the representation and use of sequencing information. We also show how these same sequencing representations and mechanisms are usable in parsing as well.

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A Connectionist Approach to the Generation of Abstracts

Cited by 10 publications

References 5 publications

Multi-topic multi-document summarization

Multi-topic multi-document summarization

Automatic text summarization based on the Global Document Annotation

Sequencing in a connectionist model of language processing

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