Using maximum entropy for sentence extraction

Osborne, Miles

doi:10.3115/1118162.1118163

Cited by 97 publications

(55 citation statements)

References 10 publications

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“…We hypothesise that this is due to the very carefully constructed feature set optimised for naïve Bayes. Results from Osborne (2002), where maximum entropy was shown to perform much better than naïve Bayes when features are highly dependent, support this hypothesis. Our results also support this hypothesis.…”

Section: Resultsmentioning

confidence: 77%

Extractive summarisation of legal texts

2007

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Abstract. We describe research carried out as part of a text summarisation project for the legal domain for which we use a new XML corpus of judgments of the UK House of Lords. These judgments represent a particularly important part of public discourse due to the role that precedents play in English law. We present experimental results using a range of features and machine learning techniques for the task of predicting the rhetorical status of sentences and for the task of selecting the most summary-worthy sentences from a document. Results for these components are encouraging as they achieve state-of-the-art accuracy using robust, automatically generated cue phrase information. Sample output from the system illustrates the potential of summarisation technology for legal information management systems and highlights the utility of our rhetorical annotation scheme as a model of legal discourse, which provides a clear means for structuring summaries and tailoring them to different types of users.

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Section: Resultsmentioning

confidence: 77%

Extractive summarisation of legal texts

2007

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“…Log Linear Model [5]: Osborne used log-linear models and showed that existing approaches used feature independence and these models produce better extracts than naïve-bayes model.…”

Section: Classification Of Automatic Text Summarizationmentioning

confidence: 99%

Literature Review on Automatic Text Summarization: Single and Multiple Summarizations

Bhatia¹,

Jaiswal²

2015

IJCA

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The online information available on world wide web is in enormous amount. Search engines like Google, Yahoo were developed to retrieve information from the databases. But actual results were not obtained as the electronic information is increasing day by day. Thus automatic summarization came into demand. Automatic summarization gathers several documents as input and provides the shorter summarized version as output which is informative, unambiguous, save valuable time. Research was done on a single document and moved towards multiple documents. This review categorizes single and multiple summarization methods.

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“…Weight learning method (Osborne, 2002) Conjugate gradient decent search method (Fattah and Ren, 2009) Mathematical Regression (MR) model (Binwahlan et al, 2009) Particle Swarm Optimization (PSO) (Dehkordi et al, 2009), Genetic Algorithm (GA) model (Bossard and Rodrigues, 2011) and (Suanmali et al, 2011) Besides optimizing feature weights, the impact of combining different features has been investigated by Hariharan (2010) for multi document summarization. In his study, the author showed that term frequency weight combined with position and node weight feature yields significantly better results.…”

Section: Authorsmentioning

confidence: 99%

Automatic Multi Document Summarization Approaches

Kumar

Salim²

2012

Journal of Computer Science

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Problem statement: Text summarization can be of different nature ranging from indicative summary that identifies the topics of the document to informative summary which is meant to represent the concise description of the original document, providing an idea of what the whole content of document is all about. Approach: Single document summary seems to capture both the information well but it has not been the case for multi document summary where the overall comprehensive quality in presenting informative summary often lacks. It is found that most of the existing methods tend to focus on sentence scoring and less consideration is given to the contextual information content in multiple documents. Results: In this study, some survey on multi document summarization approaches has been presented. We will direct our focus notably on four well known approaches to multi document summarization namely the feature based method, cluster based method, graph based method and knowledge based method. The general ideas behind these methods have been described. Conclusion: Besides the general idea and concept, we discuss the benefits and limitations concerning these methods. With the aim of enhancing multi document summarization, specifically news documents, a novel type of approach is outlined to be developed in the future, taking into account the generic components of a news story in order to generate a better summary.

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Using maximum entropy for sentence extraction

Cited by 97 publications

References 10 publications

Extractive summarisation of legal texts

Extractive summarisation of legal texts

Literature Review on Automatic Text Summarization: Single and Multiple Summarizations

Automatic Multi Document Summarization Approaches

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