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Elkiss, Aaron; Shen, Siwei; Fader, Anthony; Erkan, Güneş; States, David J.; Radev, Dragomir

doi:10.1002/asi.v59:1

Cited by 60 publications

(4 citation statements)

References 300 publications

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“…Elkiss et.al. [3] provided a quantitative analysis of the benefits of citation contexts with regards to similar applications, such as summarisation and information retrieval. In particular, they examined the relationship between the abstract and citation contexts of a given scientific paper.…”

Section: Applications Of Citation Contextsmentioning

confidence: 99%

“…Our work is related to [5,4,3], who used citation terms with original full-text to boost systems such as IR and text summarisation. The main differences of our approach are our application task (text classification), the implementation of intrinsic evaluation, and the reliance on sophisticated term-weighting models based on a variety of parameters: sections that the terms come from, distance to the citation markers, semantic relationships from a knowledge-base, and window size.…”

Section: Applications Of Citation Contextsmentioning

confidence: 99%

“…Previous work has identified the usefulness of this source of information for applications such as Text Mining [1,2,3], and Information Retrieval (IR) [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Improving MeSH classification of biomedical articles using citation contexts

Aljaber

Martínez

Stokes

et al. 2011

Journal of Biomedical Informatics

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Medical Subject Headings (MeSH) are used to index the majority of databases generated by the National Library of Medicine. Essentially, MeSH terms are designed to make information, such as scientific articles, more retrievable and assessable to users of systems such as PubMed. This paper proposes a novel method for automating the assignment of biomedical publications with MeSH terms that takes advantage of citation references to these publications. Our findings show that analysing the citation references that point to a document can provide a useful source of terms that are not present in the document. The use of these citation contexts, as they are known, can thus help to provide a richer document feature representation, which in turn can help improve text mining and information retrieval applications, in our case MeSH term classification. In this paper, we also explore new methods of selecting and utilising citation contexts. In particular, we assess the effect of weighting the importance of citation terms (found in the citation contexts) according to two aspects: (i) the section of the paper they appear in and (ii) their distance to the citation marker. We conduct intrinsic and extrinsic evaluations of citation term quality. For the intrinsic evaluation, we rely on the UMLS Metathesaurus conceptual database to explore the semantic characteristics of the mined citation terms. We also analyse the "informativeness" of these terms using a class-entropy measure. For the extrinsic evaluation, we run a series of automatic document classification experiments over MeSH terms. Our experimental evaluation shows that citation contexts contain terms that are related to the original document, and that the integration of this knowledge results in better classification performance compared to two state-of-the-art MeSH classification systems: MeSHUP and MTI. Our experiments also demonstrate that the consideration of Section and Distance factors can lead to statistically significant improvements in citation feature quality, thus opening the way for better document feature representation in other biomedical text processing applications.

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Section: Applications Of Citation Contextsmentioning

confidence: 99%

Section: Applications Of Citation Contextsmentioning

confidence: 99%

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Improving MeSH classification of biomedical articles using citation contexts

Aljaber

Martínez

Stokes

et al. 2011

Journal of Biomedical Informatics

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“…Scientific papers summarization goes back more than thirty years. Some of these works focus on summarizing content (Paice, 1981;Paice and Jones, 1993), while others focused on citation sentences (citation-aware summarization) (Elkiss et al, 2008;Qazvinian and Radev, 2008;Abu-Jbara and Radev, 2011). Recently, Yasunaga et al (2019) released a large-scale dataset, Scisumm-Net, including summaries produced by humans for over 1000 scientific papers using solely the papers abstract and citations.…”

Section: Ingestion Pipelinementioning

confidence: 99%

A Summarization System for Scientific Documents

Erera¹,

Shmueli-Scheuer²,

Feigenblat³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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We present a novel system providing summaries for Computer Science publications. Through a qualitative user study, we identified the most valuable scenarios for discovery, exploration and understanding of scientific documents. Based on these findings, we built a system that retrieves and summarizes scientific documents for a given information need, either in form of a free-text query or by choosing categorized values such as scientific tasks, datasets and more. Our system ingested 270,000 papers, and its summarization module aims to generate concise yet detailed summaries. We validated our approach with human experts.

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