SimSum: an empirically founded simulation of summarizing

Endres-Niggemeyer, Brigitte

doi:10.1016/s0306-4573(99)00066-7

Cited by 17 publications

(8 citation statements)

References 12 publications

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“…The method only dealt with removal of elements and used a very small corpus of compressed sentences, it was therefore extended in [76]. In professional abstracting settings [13], it was observed that in order to produce abstracts, textual fragments are usually combined to create new sentences and sometimes new linguistic material is included. Simulation of text abstracting operations have been implemented using rule-based and machine learning approaches in [21,61,62].…”

Section: Non-extractive Methodsmentioning

confidence: 99%

Automatic Text Summarization: Past, Present and Future

Saggion

Poibeau

2012

Theory and Applications of Natural Language Processing

125

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Automatic text summarization, the computer-based production of condensed versions of documents, is an important technology for the information society. Without summaries it would be practically impossible for human beings to get access to the ever growing mass of information available online. Although research in text summarization is over fifty years old, some efforts are still needed given the insufficient quality of automatic summaries and the number of interesting summarization topics being proposed in different contexts by end users ("domain-specific summaries", "opinion-oriented summaries", "update summaries", etc.). This paper gives a short overview of summarization methods and evaluation.

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Section: Non-extractive Methodsmentioning

confidence: 99%

Automatic Text Summarization: Past, Present and Future

Saggion

Poibeau

2012

Theory and Applications of Natural Language Processing

125

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“…In fact, this research's main goal is to extract sentences for final summary based on some selection features that signals importance of some sentences. Related research has shown that when humans perform the process of summarizing a document they tend to use readymade text passages -extracts- [78], where 80% of sentences in their generated Moreover, extractive approaches tend to be faster, easily extendable and retain most of the structure of the original document instead of flattening it. However, its main disadvantage is that by using certain techniques, it may become misleading in terms of structure.…”

Section: Proposed Methodology Overviewmentioning

confidence: 99%

Frequently Asked Questions Web Pages Automatic Text Summarization

Shaalan¹,

Rafea²

2011

Artificial Intelligence and Applications / 718: Modelling, Identification, and Control

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This research is directed towards automating frequently asked questions Web pages summarization, a task that captures the most salient pieces of information in answers of each question. In fact, it was found that there exists thousands of FAQ pages available on many subjects not only online but also offline as most of products nowadays tend to attach a leaflet tilted as FAQ along with its product describing their functionality and usage. Moreover, FAQ Web pages are devised into a manner of a question having a specific heading style, e.g. bold, underlines, or tagged. The answer then follows in a different lower style; usually smaller font and may be scattered to subheadings if the answer is logically divided. This uniformity in the creation of FAQ Web pages and its abundance online, made it pretty much amenable to automation. To achieve this objective, an approach, which applies Web page segmentation to detect Q/A-Question and Answer-units along with the use of some selective statistical sentence extraction features for summary generation, is proposed. The proposed approach is English language dependent. Copernic Good Entrants are provided information on how to submit their recordings electronically for consideration. 4-Who can vote ? FAQWEBSUMM Excellent Recording Academy voting members only. Copernic Very Bad None 5-Who qualifies as a Voting Member ? FAQWEBSUMM Excellent Recording Academy voting members are professionals with creative or technical credits on six commercially released tracks (or their equivalent) .

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“…Related research has shown that human abstractors use readymade text passages from source document for summarization (Endres‐Niggemeyer, 2002). Eighty percent of the sentences in the manmade abstracts were closely matched with sentences in source documents (Kupiec, Pedersen, & Chen, 1995).…”

Section: Automatic Text Summarization Using Salient Featuresmentioning

confidence: 99%

Hierarchical summarization of large documents

Yang

Wang

2008

J. Am. Soc. Inf. Sci.

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Many automatic text summarization models have been developed in the last decades. Related research in information science has shown that human abstractors extract sentences for summaries based on the hierarchical structure of documents; however, the existing automatic summarization models do not take into account the human abstractor's behavior of sentence extraction and only consider the document as a sequence of sentences during the process of extraction of sentences as a summary. In general, a document exhibits a well-defined hierarchical structure that can be described as fractalsmathematical objects with a high degree of redundancy. In this article, we introduce the fractal summarization model based on the fractal theory. The important information is captured from the source document by exploring the hierarchical structure and salient features of the document. A condensed version of the document that is informatively close to the source document is produced iteratively using the contractive transformation in the fractal theory. The fractal summarization model is the first attempt to apply fractal theory to document summarization. It significantly improves the divergence of information coverage of summary and the precision of summary. User evaluations have been conducted. Results have indicated that fractal summarization is promising and outperforms current summarization techniques that do not consider the hierarchical structure of documents.

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SimSum: an empirically founded simulation of summarizing

Cited by 17 publications

References 12 publications

Automatic Text Summarization: Past, Present and Future

Automatic Text Summarization: Past, Present and Future

Frequently Asked Questions Web Pages Automatic Text Summarization

Hierarchical summarization of large documents

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