A semantic graph-based approach to biomedical summarisation

Plaza, Laura; Díaz, Alberto; Gervás, Pablo

doi:10.1016/j.artmed.2011.06.005

Cited by 69 publications

(64 citation statements)

References 17 publications

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“…Plaza et al compared their summarization output with abstracts included in the articles. (15) Sarkar et al compared the performance of their proposed summarization system and employed as a reference standard the output of a broad summarization system called MEAD. (40) Another common set of metrics and software package used for evaluating automatic summaries is ROUGE (Recall-Oriented Understudy for Gisting Evaluation), developed by the University of Southern California.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Text summarization in the biomedical domain: A systematic review of recent research

Mishra

Bian

Fiszman

et al. 2014

Journal of Biomedical Informatics

175

130

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Objective The amount of information for clinicians and clinical researchers is growing exponentially. Text summarization reduces information as an attempt to enable users to find and understand relevant source texts more quickly and effortlessly. In recent years, substantial research has been conducted to develop and evaluate various summarization techniques in the biomedical domain. The goal of this study was to systematically review recent published research on summarization of textual documents in the biomedical domain. Materials and methods MEDLINE (2000 to October 2013), IEEE Digital Library, and the ACM Digital library were searched. Investigators independently screened and abstracted studies that examined text summarization techniques in the biomedical domain. Information is derived from selected articles on five dimensions: input, purpose, output, method and evaluation. Results Of 10,786 studies retrieved, 34 (0.3%) met the inclusion criteria. Natural Language processing (17; 50%) and a Hybrid technique comprising of statistical, Natural language processing and machine learning (15; 44%) were the most common summarization approaches. Most studies (28; 82%) conducted an intrinsic evaluation. Discussion This is the first systematic review of text summarization in the biomedical domain. The study identified research gaps and provides recommendations for guiding future research on biomedical text summarization. conclusion Recent research has focused on a Hybrid technique comprising statistical, language processing and machine learning techniques. Further research is needed on the application and evaluation of text summarization in real research or patient care settings.

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Section: Discussionmentioning

confidence: 99%

“…They identified concepts and relations which were derived from the Unified Medical language Systems (UMLS) to construct a sematic graph and then applied a clustering algorithm to identify different themes and topics within the text to extract salient sentences for summarization. (15)…”

Section: Methodsmentioning

confidence: 99%

Text summarization in the biomedical domain: A systematic review of recent research

Mishra

Bian

Fiszman

et al. 2014

Journal of Biomedical Informatics

175

130

View full text Add to dashboard Cite

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“…Se usa un clasificador supervisado, a saber, Support Vector Machine (Vapnik, 1998) para identificar las tripletas importantes que conformarán al resumen. Otro método basado en la información semántica del texto hace uso de grafos de conceptos (Plaza, 2010;Plaza, Díaz & Gervás, 2011). En este enfoque, se usa una representación conceptual del texto basándose en una red semántica y un tesauro.…”

Section: Trabajo Relacionadounclassified

Generación de resúmenes por medio de síntesis de grafos conceptuales

Miranda¹,

Gelbukh²,

Сидоров³

2014

Rev. signos

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ResumenEn esta investigación, proponemos un modelo para la generación de resúmenes abstractivos de un solo documento, basado en la representación conceptual del texto. Aunque hay investigaciones que toman en cuenta la representación sintáctica o semántica parcial del texto, hasta ahora, una representación semántica completa de textos no se ha usado para la generación de resúmenes. Nuestro modelo usa una representación semántica completa del texto por medio de estructuras de grafos conceptuales. En este contexto, la tarea de la generación del resumen se reduce a resumir el conjunto de los grafos conceptuales correspondientes. Para realizar esto, se aplica un conjunto de operaciones sobre los grafos: generalización, unión o asociación, ponderación y poda. Además, se usan una jerarquía de conceptos (WordNet) y reglas heurísticas basadas en los patrones semánticos de VerbNet para apoyar a las operaciones. El conjunto resultante de grafos representa al resumen del texto a nivel conceptual. El método se evaluó con la colección de datos DUC 2003. Los resultados muestran que el método es efectivo para resumir textos cortos.Palabras Clave: Resúmenes abstractivos, grafos conceptuales ponderados, algoritmos de ponderación basada en grafos, algoritmo HITS.

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“…Plaza et al [16] applied heuristic rules with a clustering algorithm to the problem of biomedical text summarization. Their work mapped terms found in a given document to UMLS concepts.…”

Section: Introductionmentioning

confidence: 99%

Mapping biological entities using the longest approximately common prefix method

2014

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BackgroundThe significant growth in the volume of electronic biomedical data in recent decades has pointed to the need for approximate string matching algorithms that can expedite tasks such as named entity recognition, duplicate detection, terminology integration, and spelling correction. The task of source integration in the Unified Medical Language System (UMLS) requires considerable expert effort despite the presence of various computational tools. This problem warrants the search for a new method for approximate string matching and its UMLS-based evaluation.ResultsThis paper introduces the Longest Approximately Common Prefix (LACP) method as an algorithm for approximate string matching that runs in linear time. We compare the LACP method for performance, precision and speed to nine other well-known string matching algorithms. As test data, we use two multiple-source samples from the Unified Medical Language System (UMLS) and two SNOMED Clinical Terms-based samples. In addition, we present a spell checker based on the LACP method.ConclusionsThe Longest Approximately Common Prefix method completes its string similarity evaluations in less time than all nine string similarity methods used for comparison. The Longest Approximately Common Prefix outperforms these nine approximate string matching methods in its Maximum F1 measure when evaluated on three out of the four datasets, and in its average precision on two of the four datasets.

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A semantic graph-based approach to biomedical summarisation

Cited by 69 publications

References 17 publications

Text summarization in the biomedical domain: A systematic review of recent research

Text summarization in the biomedical domain: A systematic review of recent research

Generación de resúmenes por medio de síntesis de grafos conceptuales

Mapping biological entities using the longest approximately common prefix method

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