Comparing different knowledge sources for the automatic summarization of biomedical literature
TLDR
It is found that, when summarizing gene-related literature, using GO, SNOMED-CT and HUGO to extract domain concepts results in significantly better summaries than using all available vocabularies in the UMLS.About:
This article is published in Journal of Biomedical Informatics.The article was published on 2014-12-01 and is currently open access. It has received 19 citations till now. The article focuses on the topics: Automatic summarization & Multi-document summarization.read more
Citations
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Journal ArticleDOI
Text summarization from legal documents: a survey
TL;DR: This paper discusses different datasets and metrics used in summarization and compare performances of different approaches, first in general and then focused to legal text, and briefly covers a few software tools used in legal text summarization.
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The challenging task of summary evaluation: an overview
TL;DR: 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.
Journal ArticleDOI
Different approaches for identifying important concepts in probabilistic biomedical text summarization.
Milad Moradi,Nasser Ghadiri +1 more
TL;DR: The results show that when the Bayesian summarizer utilizes the feature selection methods that do not use the raw frequency, it can outperform the biomedical summarizers that rely on the frequency of concepts, domain-independent and baseline methods.
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Deep contextualized embeddings for quantifying the informative content in biomedical text summarization.
TL;DR: It is demonstrated that a hybrid system combining a deep bidirectional language model and a clustering method yields state-of-the-art results without requiring labor-intensive creation of annotated features or knowledge bases or computationally demanding domain-specific pretraining.
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Graph-based biomedical text summarization: An itemset mining and sentence clustering approach.
TL;DR: A novel graph-based summarization method that takes advantage of the domain-specific knowledge and a well-established data mining technique called frequent itemset mining to address the informativeness measurement of the sentences.
References
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Journal ArticleDOI
Gene Ontology: tool for the unification of biology
M Ashburner,Catherine A. Ball,Judith A. Blake,David Botstein,Heather Butler,J. M. Cherry,Allan Peter Davis,Kara Dolinski,Selina S. Dwight,J.T. Eppig,Midori A. Harris,David P. Hill,Laurie Issel-Tarver,Andrew Kasarskis,Suzanna E. Lewis,John C. Matese,Joel E. Richardson,M. Ringwald,Gerald M. Rubin,Gavin Sherlock +19 more
TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Journal ArticleDOI
Community structure in social and biological networks
Michelle Girvan,Mark Newman +1 more
TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Proceedings Article
ROUGE: A Package for Automatic Evaluation of Summaries
TL;DR: Four different RouGE measures are introduced: ROUGE-N, ROUge-L, R OUGE-W, and ROUAGE-S included in the Rouge summarization evaluation package and their evaluations.
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The automatic creation of literature abstracts
TL;DR: In the exploratory research described, the complete text of an article in machine-readable form is scanned by an IBM 704 data-processing machine and analyzed in accordance with a standard program.
Journal ArticleDOI
LexRank: graph-based lexical centrality as salience in text summarization
Gunes Erkan,Dragomir R. Radev +1 more
TL;DR: LexRank as discussed by the authors is a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing (NLP), which is based on the concept of eigenvector centrality.