Automatic multidocument summarization of research abstracts: Design and user evaluation

Sophisticated documents like legal cases and biomedical articles can contain unusually long sentences. Extractive summarizers can select such sentences—potentially adding hundreds of unnecessary words to the summary—or exclude them and lose important content. Sentence simplification or compression seems on the surface to be a promising solution. However, compression removes words before the selection algorithm can use them, and simplification generates sentences that may be ambiguous in an extractive summary. We therefore compare the performance of an extractive summarizer selecting from the sentences of the original document with that of the summarizer selecting from sentences shortened in three ways: simplification, compression, and disaggregation, which splits one sentence into several according to rules designed to keep all meaning. We find that on legal cases and biomedical articles, these shortening methods generate ungrammatical output. Human evaluators performed an extrinsic evaluation consisting of comprehension questions about the summaries. Evaluators given compressed, simplified, or disaggregated versions of the summaries answered fewer questions correctly than did those given summaries with unaltered sentences. Error analysis suggests 2 causes: Altered sentences sometimes interact with the sentence selection algorithm, and alterations to sentences sometimes obscure information in the summary. We discuss future work to alleviate these problems.

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“…As noted by Ou et al. (), human participants can judge a summary directly, rather than compare it to an “ideal” summary.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Sentence simplification, compression, and disaggregation for summarization of sophisticated documents

Finegan-Dollak

Radev

2015

Asso for Info Science & Tech

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“…Regarding the output, a summary may be an extract (i.e. when a selection of "significant" sentences of a document is shown) (Zhang and Fung 2009) is added (Ou, Khoo and Goh 2007), or even a headline (or title) (Sarkar and Bandyopadhyay 2005;Hennig 2009). It is also possible to distinguish between generic summaries and query-focused summaries (also known as user-focused or topic-focused).…”

Section: Common Factors For Classifying Summariesmentioning

confidence: 99%

COMPENDIUM: a text summarisation tool for generating summaries of multiple purposes, domains, and genres

Lloret¹,

Palomar²

2012

Nat. Lang. Eng.

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In this paper, we present a Text Summarisation tool, compendium, capable of generating the most common types of summaries. Regarding the input, single-and multi-document summaries can be produced; as the output, the summaries can be extractive or abstractiveoriented; and finally, concerning their purpose, the summaries can be generic, query-focused, or sentiment-based. The proposed architecture for compendium is divided in various stages, making a distinction between core and additional stages. The former constitute the backbone of the tool and are common for the generation of any type of summary, whereas the latter are used for enhancing the capabilities of the tool. The main contributions of compendium with respect to the state-of-the-art summarisation systems are that (i) it specifically deals with the problem of redundancy, by means of textual entailment; (ii) it combines statistical and cognitive-based techniques for determining relevant content; and (iii) it proposes an abstractive-oriented approach for facing the challenge of abstractive summarisation. The evaluation performed in different domains and textual genres, comprising traditional texts, as well as texts extracted from the Web 2.0, shows that compendium is very competitive and appropriate to be used as a tool for generating summaries.

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“…Moreover, although most of the approaches still rely on a sentence-extraction paradigm where several features are employed to determine the importance of sentences in documents and then select and extract the most relevant ones to build the summary (Mani, 2001), the improvement of natural language generation and sentence fusion and simplification methods are encouraging approaches to generate summaries following an abstractive strategy. Examples of these types of summaries can be found in (Ou et al, 2007), (Sauper and Barzilay, 2009), and (Saggion, 2009) to name a few. Another issue that is changing in the TS field is the domain of the documents for generating the summaries.…”

Section: Text Summarizationmentioning

confidence: 99%

Towards a unified framework for opinion retrieval, mining and summarization

et al. 2012

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The exponential increase of subjective, user-generated content since the birth of the Social Web, has led to the necessity of developing automatic text processing systems able to extract, process and present relevant knowledge. In this paper, we tackle the Opinion Retrieval, Mining and Summarization task, by proposing a unified framework, composed of three crucial components (information retrieval, opinion mining and text summarization) that allow the retrieval, classification and summarization of subjective information. An extensive analysis is conducted, where different configurations of the framework are suggested and analyzed, in order to determine which is the best one, and under which conditions. The evaluation carried out and the results obtained shows the appropriateness of the individual components, as well as the framework as a whole. By achieving an improvement over 10% compared to the state-of-the-art approaches in the context of blogs, we can conclude that subjective text can be efficiently dealt with by means of our proposed framework.

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Automatic multidocument summarization of research abstracts: Design and user evaluation

Cited by 8 publications

References 18 publications

Sentence simplification, compression, and disaggregation for summarization of sophisticated documents

Sentence simplification, compression, and disaggregation for summarization of sophisticated documents

COMPENDIUM: a text summarisation tool for generating summaries of multiple purposes, domains, and genres

Towards a unified framework for opinion retrieval, mining and summarization

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