Automatic Text Summarization: A State-of-the-Art Review

Klymenko, Oleksandra; Braun, Daniel A.; Matthes, Florian

doi:10.5220/0009723306480655

Cited by 14 publications

(5 citation statements)

References 20 publications

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“…In this study, we presented a fully data-driven approach for automatic text summarization. We proposed and evaluated the model on unstructured datasets which show some results comparable to the current state-of-the-art topic modelling techniques without depending on modifications using any linguistic information models [34]. Manual summarization is laborious and challenging task to accomplish.…”

Section: Discussionmentioning

confidence: 98%

A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling

Onah

Pang

El-Haj

2022

2022 IEEE International Conference on Big Data (Big Data)

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With the advent and popularity of big data mining and huge text analysis in modern times, automated text summarization became prominent for extracting and retrieving important information from documents. This research investigates aspects of automatic text summarization from the perspectives of single and multiple documents. Summarization is a task of condensing huge text articles into short, summarized versions. The text is reduced in size for summarization purpose but preserving key vital information and retaining the meaning of the original document. This study presents the Latent Dirichlet Allocation (LDA) approach used to perform topic modelling from summarised medical science journal articles with topics related to genes and diseases. In this study, P yLDAvis webbased interactive visualization tool was used to visualise the selected topics. The visualisation provides an overarching view of the main topics while allowing and attributing deep meaning to the prevalence individual topic. This study presents a novel approach to summarization of single and multiple documents. The results suggest the terms ranked purely by considering their probability of the topic prevalence within the processed document using extractive summarization technique. P yLDAvis visualization describes the flexibility of exploring the terms of the topics' association to the fitted LDA model. The topic modelling result shows prevalence within topics 1 and 2. This association reveals that there is similarity between the terms in topic 1 and 2 in this study. The efficacy of the LDA and the extractive summarization methods were measured using Latent Semantic Analysis (LSA) and Recall-Oriented Understudy for Gisting Evaluation (ROU GE) metrics to evaluate the reliability and validity of the model.

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Section: Discussionmentioning

confidence: 98%

A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling

Onah

Pang

El-Haj

2022

2022 IEEE International Conference on Big Data (Big Data)

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“…-2018Tandel et al (2019 It surveys the neural network-based abstractive text summarization approaches. 2014-2018 Klymenko et al (2020) It presents a general overview of summarization methods, including recent trends. 1958-2020 Awasthi et al (2021) It presents a general overview of summarization methods including very recent works.…”

Section: Focusmentioning

confidence: 99%

A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding

Cajueiro¹,

Nery²,

Tavares³

et al. 2023

Preprint

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We provide a literature review about Automatic Text Summarization (ATS) systems. We consider a citation-based approach. We start with some popular and well-known papers that we have in hand about each topic we want to cover and we have tracked the "backward citations" (papers that are cited by the set of papers we knew beforehand) and the "forward citations" (newer papers that cite the set of papers we knew beforehand). In order to organize the different methods, we present the diverse approaches to ATS guided by the mechanisms they use to generate a summary. Besides presenting the methods, we also present an extensive review of the datasets available for summarization tasks and the methods used to evaluate the quality of the summaries. Finally, we present an empirical exploration of these methods using the CNN Corpus dataset that provides golden summaries for extractive and abstractive methods.

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“…En este tópico se destacan trabajos como el de Cohen et al (2009), quienes determinan cómo los métodos automatizados de RS se pueden mejorar utilizando datos de entrenamiento de los modelos de otras revisiones. En el trabajo de Klymenko et al (2020), se presenta una revisión de los métodos de ML utilizados en el proceso de síntesis de texto. Otro trabajo relevante es el de Walker et al (2022), donde se presentan los resultados de la aplicación de la herramienta Dextr, diseñada para extracción semiautomatizada de texto en publicaciones clínicas.…”

Section: Preproceso De Los Datosunclassified

Modelado de tópicos aplicado al análisis del papel del aprendizaje automático en revisiones sistemáticas

Aguirre

Figueroa-Vallejo

2022

rev. investig. desarro. innov

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El objetivo de la investigación fue analizar el papel del aprendizaje automático de datos en las revisiones sistemáticas de literatura. Se aplicó la técnica de Procesamiento de Lenguaje Natural denominada modelado de tópicos, a un conjunto de títulos y resúmenes recopilados de la base de datos Scopus. Especificamente se utilizó la técnica de Asignación Latente de Dirichlet (LDA), a partir de la cual se lograron descubrir y comprender las temáticas subyacentes en la colección de documentos. Los resultados mostraron la utilidad de la técnica utilizada en la revisión exploratoria de literatura, al permitir agrupar los resultados por temáticas. Igualmente, se pudo identificar las áreas y actividades específicas donde más se ha aplicado el aprendizaje automático, en lo referente a revisiones de literatura. Se concluye que la técnica LDA es una estrategia fácil de utilizar y cuyos resultados permiten abordar una amplia colección de documentos de manera sistemática y coherente, reduciendo notablemente el tiempo de la revisión.

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Automatic Text Summarization: A State-of-the-Art Review

Cited by 14 publications

References 20 publications

A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling

A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling

A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding

Modelado de tópicos aplicado al análisis del papel del aprendizaje automático en revisiones sistemáticas

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