Automatic summarization for text simplification

Margarido, Paulo R. A.; Pardo, Thiago Alexandre Salgueiro; Antonio, Gabriel; Fuentes, Vinícius B.; Aires, Rachel; Aluísio, Sandra Maria; Fortes, Renata Pontin de Mattos

doi:10.1145/1809980.1810057

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…Some efforts have been made on summarisation as simplification. Margarido, Pardo, Antonio, Fuentes, Aires, Aluísio, and Fortes (2008) tested three different extraction-based summarisation strategies on target readers, and found that all strategies improved the understanding of the text to some extent. They concluded that summarisation, in combination with other techniques, could be useful for simplifying texts, but that it is important to take the literacy level of the reader into account.…”

Section: Automatic Text Summarisationmentioning

confidence: 99%

Automatic Adaptation of Swedish Text for Increased Inclusion

Rennes

2022

Linköping Studies in Science and Technology. Dissertations

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Imagine what life would be if words did not make any sense to you. If you had to use a dictionary, or Google Translate, to check the meaning of each word. If you forgot the first part of the sentence when you got to the last word. Working would be hard, studying perhaps even more difficult. Reading is a skill essential for participation in many parts of the modern society, but there are many individuals incapable of assimilating text. This could be for various reasons, for instance due to dyslexia, cognitive disabilities, or as a consequence of having a different first language. In order to automate the adaptation process, more knowledge about the properties of Easy Language texts is needed. There are several collections of guidelines aimed at writers of Easy Language texts. Such guidelines are useful for getting an idea of what Easy Language should be, but they are often rather vague and rely on the expert knowledge and intuition of the writer. By analysing texts produced by writers of Easy Language, it is possible to operationalise the guidelines and obtain data for building models for automatic text adaptation. However, this approach requires large corpora of high quality, and the results are highly dependent on the training data. This doctoral thesis adopts an end-user approach to automatic text adaptation. The application of a reading comprehension perspective on Easy Language text adaptation differs from the text-based perspectives commonly used within the field and this position implies that the strengths and weaknesses of individuals in the different groups of poor readers are important to consider. We present and evaluate a variety of techniques for automatic text adaptation, as well as work on text complexity visualisation. Assessment of text complexity can be useful for both readers and writers of Easy Language texts. However, common complexity features are not easily interpretable, and this thesis presents work on enhancing interpretability of such features, using clustering and visualisation methods. The main contributions of this thesis are 1) a mapping of Easy Language guidelines to a theoretical model of reading comprehension, 2) a corpus of simple and standard documents, aligned at the sentence level, 3) clustering and visualisation of text complexity measures, and 4) a number of tools and services for the automatic adaptation of Swedish text.iii They say that doing a PhD is a journey. I always thought it was somewhat of a cliche, but approaching the finish line, I understand completely. It has really been a journey for me, both professionally and personally.First and foremost, I would like to thank my supervisor Arne Jönsson. You have provided me with much-needed inspiration and motivation and you have helped me to believe in myself. Where I see problems, you see solutions and opportunities. I have always felt your support, knowing that you were only two doors away (or lately, only a Teams call away), always taking time for me when needed.Thank you, Marina Santini, my secondary supervisor, for y...

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Section: Automatic Text Summarisationmentioning

confidence: 99%

Automatic Adaptation of Swedish Text for Increased Inclusion

Rennes

2022

Linköping Studies in Science and Technology. Dissertations

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“…The advances in computer vision means that multimodal summarisation is more feasible now, with systems that are able to caption images (Tanti, Gatt, and Camilleri, 2018) or are able to summarise complex sentences with images and other graphical representations (UzZaman, Bigham, and Allen, 2011). Methods from automatic summarisation also proved useful for text simplification (Margarido, Pardo, Antonio, Fuentes, Aires, Aluísio, and Fortes, 2008).…”

Section: Orȃsanmentioning

confidence: 99%

Automatic summarisation: 25 years On

Orăsan

2019

Nat. Lang. Eng.

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Automatic text summarisation is a topic that has been receiving attention from the research community from the early days of computational linguistics, but it really took off around 25 years ago. This article presents the main developments from the last 25 years. It starts by defining what a summary is and how its definition changed over time as a result of the interest in processing new types of documents. The article continues with a brief history of the field and highlights the main challenges posed by the evaluation of summaries. The article finishes with some thoughts about the future of the field.

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“…It is an open-source application to generate multilingual extractive summaries that can be downloaded online. 7 This tool allows constructing extractive summaries based on the detection of the main ideas from the source document, considering the reduction of redundant information.…”

Section: Open Text Summarizer (Ots)mentioning

confidence: 99%

“…Since 1993, the researchers of NILC and others have been performed several applications for Portuguese ATS. Some of them include the use of supervised and unsupervised machine learning methods [1,2], discursive knowledge models [3][4][5], identification of "gist sentence" from the source documents to generate extractive summaries [6], Text Simplification (TS) [7], complex networks and graph-based methods to text analysis [8][9][10][11]. On the other hand, some ATS systems have been proposed to generate extractive summaries through optimization-based methods [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Calculating the Upper Bounds for Portuguese Automatic Text Summarization Using Genetic Algorithm

Rojas-Simón

Ledeneva

García-Hernández

2018

Advances in Artificial Intelligence - IBERAMIA 2018

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Over the last years, Automatic Text Summarization (ATS) has been considered as one of the main tasks in Natural Language Processing (NLP) that generates summaries in several languages (e.g., English, Portuguese, Spanish, etc.). One of the most significant advances in ATS is developed for Portuguese reflected with the proposals of various state-of-art methods. It is essential to know the performance of different state-of-the-art methods with respect to the upper bounds (Topline), lower bounds (Baseline-random), and other heuristics (Baseline-first). In recent works, the significance and upper bounds for Single-Document Summarization (SDS) and Multi-Document Summarization (MDS) using corpora from Document Understanding Conferences (DUC) were calculated. In this paper, a calculus of upper bounds for SDS in Portuguese using Genetic Algorithms (GA) is performed. Moreover, we present a comparison of some state-of-the-art methods with respect to the upper bounds, lower bounds, and heuristics to determinate their level of significance.

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Automatic summarization for text simplification

Cited by 10 publications

References 19 publications

Automatic Adaptation of Swedish Text for Increased Inclusion

Automatic Adaptation of Swedish Text for Increased Inclusion

Automatic summarisation: 25 years On

Calculating the Upper Bounds for Portuguese Automatic Text Summarization Using Genetic Algorithm

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