Automatic summarising: The state of the art

Jones, Karen Spärck

doi:10.1016/j.ipm.2007.03.009

Cited by 257 publications

(44 citation statements)

References 61 publications

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“…Different studies suggest that, when humans had to decide the most relevant sentences from documents in order to produce summaries, they frequently disagree in which sentences best represent the content of a document (Spärck Jones, 2007;McKeown et al, 2001). However, although the low agreement between humans is a problem, new evaluation methods and tools (such as the Pyramid scheme previously described) appear to offer a way to reduce disagreement.…”

Section: Discussionmentioning

confidence: 99%

The challenging task of summary evaluation: an overview

Lloret

Plaza

Aker

2017

Lang Resources & Evaluation

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Evaluation is crucial in the research and development of automatic summarization applications, in order to determine the appropriateness of a summary based on different criteria, such as the content it contains, and the way it is presented. To perform an adequate evaluation is of great relevance to ensure that automatic summaries can be useful for the context and/or application they are generated for. To this end, researchers must be aware of the evaluation metrics, approaches, and datasets that are available, in order to decide which of them would be the most suitable to use, or to be able to propose new ones, overcoming the possible limitations that existing methods may present. In this article, a critical and historical analysis of evaluation metrics, methods, and datasets for automatic summarization systems is presented, where the strengths and weaknesses of evaluation efforts are discussed and the major challenges to solve are identified. Therefore, a clear up-to-date overview of the evolution and progress of summarization evaluation is provided, giving the reader useful insights into the past, present and latest trends in the automatic evaluation of summaries.

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Section: Discussionmentioning

confidence: 99%

The challenging task of summary evaluation: an overview

Lloret

Plaza

Aker

2017

Lang Resources & Evaluation

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“…Given the large variety of textual input that is undergoing summarization, and the different requirements the summaries are to fulfil, there is a large number of approaches to summarization evaluation (Spärck Jones 2007;Mani 2001;Nenkova and McKeown 2011;Jurafsky and Martin 2009). Automatic Summarization, along with its evaluation, has come a long way since its first applications in the 1950s, but it is still a growing field.…”

Section: Summary and Outlook With Some Short Remarks On Summarizationmentioning

confidence: 99%

Linguistic challenges in automatic summarization technology

Diedrichsen¹

2017

J. Comp. Assist. Linguist. Res.

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Automatic summarization is a field of Natural Language Processing that is increasingly used in industry today. The goal of the summarization process is to create a summary of one document or a multiplicity of documents that will retain the sense and the most important aspects while reducing the length considerably, to a size that may be user-defined. One differentiates between extraction-based and abstraction-based summarization. In an extraction-based system, the words and sentences are copied out of the original source without any modification. An abstractionbased summary can compress, fuse or paraphrase sections of the source document. As of today, most summarization systems are extractive. Automatic document summarization technology presents interesting challenges for Natural Language Processing. It works on the basis of coreference resolution, discourse analysis, named entity recognition (NER), information extraction (IE), natural language understanding, topic segmentation and recognition, word segmentation and part-of-speech tagging. This study will overview some current approaches to the implementation of auto summarization technology and discuss the state of the art of the most important NLP tasks involved in them. We will pay particular attention to current methods of sentence extraction and compression for single and multi-document summarization, as these applications are based on theories of syntax and discourse and their implementation therefore requires a solid background in linguistics. Summarization technologies are also used for image collection summarization and video summarization, but the scope of this paper will be limited to document summarization.

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“…A review research in automatic summarizing in the last decade is presented in [4] which concludes that automatic summarization techniques has made steady progress, with better evaluation and better tools & applications, but their research stated the poor motivation of summarizing systems in relation to the factors affecting them.…”

Section: Related Workmentioning

confidence: 99%

Summarizing Services of Java Packages

Hammad¹,

Abuljadayel²,

Khalaf³

2016

LNSE

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Abstract-Program comprehension is essential for code maintenance and evolution activities. It saves time and efforts of developers who want to perform any code changes. It also minimizes the chances of introducing bugs. Textual summaries for source code provide great help to code understanding activities. This paper presents an approach to automatically generate textual summaries for services implemented in java packages. The summary is generated by analyzing the source code of methods defined the package. Each method represents a service provide by the package. Each service is summarized as a natural language textual description. The generated summary for a method mainly includes the used data and the names of invoked methods. Summaries of all methods defined in a package are refined and integrated to be reported as a comprehensive summary for the services provided by the package. The generated summaries are useful in different ways. They can be used by developers in their maintenance activities. They also can be useful for the documentation purposes. IndexTerms-Program comprehension, software maintenance, source code summarization.

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Automatic summarising: The state of the art

Cited by 257 publications

References 61 publications

The challenging task of summary evaluation: an overview

The challenging task of summary evaluation: an overview

Linguistic challenges in automatic summarization technology

Summarizing Services of Java Packages

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