A text summarization method based on fuzzy rules and applicable to automated assessment

Goularte, Fábio Bif; Nassar, Sílvia Modesto; Fileto, Renato; Saggion, Horacio

doi:10.1016/j.eswa.2018.07.047

Cited by 77 publications

(33 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They suggest that the combination of different techniques (or a hybrid model) of summarization in a systematic manner can enhance the performance of the summarization system. Goularte et al (2019) discuss a method for text summarization using the fuzzy rules which is further used for automatic text assessment. They show that the fuzzy based summarization system can successfully enhance the quality of the generated summaries.…”

Section: Other Methodsmentioning

confidence: 99%

A Review on Text Summarization Techniques

Verma¹,

Verma²

2020

JSR

View full text Add to dashboard Cite

In recent years, an enormous amount of text data from diversified sources has been emerged day-by-day. This huge amount of data carries essential information and knowledge that needs to be effectively summarized to be useful. Hence, the main contribution of this paper is twofold. We first introduce some concepts related to extractive text summarization and then provide a systematic analysis of various text summarization techniques. In particular, some challenges in extractive summarization of single as well as multiple documents are introduced. The problems focus on the textual assessment and similarity measurement between the text documents are addressed. The challenges discussed are generic and applicable to every possible scenario in text summarization. Then, existing state-of-the-art of extractive summarization techniques are discussed that focus on the identified challenges.

show abstract

Section: Other Methodsmentioning

confidence: 99%

A Review on Text Summarization Techniques

Verma¹,

Verma²

2020

JSR

View full text Add to dashboard Cite

show abstract

“…It gives reasonable precision along with simplicity and computational efficiency [27]. For sentence-based feature extraction, Fuzzy C-means clustering has been used as compared to other features such as length, position, title word, thematic word and others as used in past work [19], [28], [29].…”

Section: To Oversee Diverse Number Of Bug Reports Several Automationmentioning

confidence: 99%

“…To produce an extractive summary various technique are used such as genetic algorithm [41], conditional random fields [42], neural networks [43], semantic similarity such as latent semantic analysis [44]- [46]. Along with these supervised learning techniques, unsupervised learning methods such as fuzzy logic [28], [29], [47], [48], k-means clustering along with term frequency-inverse document frequency [49] has been used. Patel et al proposed a method for multi-document summarization using fuzzy logic.…”

Section: A Extractive Summarizationmentioning

confidence: 99%

Automatic Keyword and Sentence-Based Text Summarization for Software Bug Reports

Jindal

Kaur

2020

IEEE Access

View full text Add to dashboard Cite

Text Summarization is a process which efficiently retrieves the relevant information from documents. The objective of the proposed, unsupervised approach is to summarize bug reports (software artefacts) with complete content and diversified information. The proposed approach utilizes Rapid Automatic Keyword Extraction and term frequency-inverse document frequency method to extract meaningful keywords and key-phrases with a relevant score. For sentence extraction, fuzzy C-means clustering is used to extracts sentences having high degree of membership from each cluster above a set threshold value. A ruleengine is used for sentence selection. The rules are generated with the domain knowledge and based on the extracted information by the keywords and sentences selected by the clustering method. Cohesive and coherent summary is generated by the proposed method on apache bug reports. For redundancy removal and to re-rank generated summary, hierarchical clustering is presented to enrich the extracted summary. The proposed approach is evaluated on newly constructed Apache project Bug Report Corpus (APBRC) and existing Bug Report Corpus (BRC). The results are compared on the basis of performance metrics such as precision, recall, pyramid precision and F-score. The experimental results depict that our proposed approach attains significant improvement over other baseline approaches such as BRC and LRCA. It also attains significant improvement over existing state-of-art unsupervised approaches such as Hurried, centroid and others. It extracts significant keyword phrases and sentences from each cluster to achieve full coverage and coherent summary. The results evaluated on APBRC corpus attains an average value of 78.22%, 82.18%, 80.10% and 81.66% for precision, recall, f-score and pyramid precision respectively. INDEX TERMS Text summarization, rapid automatic keyword extraction, fuzzy c-means, hierarchical clustering, bug reports, rule engine.

show abstract

“…Reference [29] proposed a text summarization model based on a LSTM-CNN framework that can construct summaries by exploring semantic phrases. Reference [30] presented an automatic process for text abstraction which was based on fuzzy rules on a variety of extracted features to find the most important information from the source text.…”

Section: Automatic Text Summarizationmentioning

confidence: 99%

Abstract Text Summarization with a Convolutional Seq2seq Model

Zhang

Wang

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

text summarization aims to offer a highly condensed and valuable information that expresses the main ideas of the text. Most previous researches focus on extractive models. In this work, we put forward a new generative model based on convolutional seq2seq architecture. A hierarchical CNN framework is much more efficient than the conventional RNN seq2seq models. We also equip our model with a copying mechanism to deal with the rare or unseen words. Additionally, we incorporate a hierarchical attention mechanism to model the keywords and key sentences simultaneously. Finally we verify our model on two real-life datasets, GigaWord and DUC corpus. The experiment results verify the effectiveness of our model as it outperforms state-of-the-art alternatives consistently and statistical significantly.

show abstract

A text summarization method based on fuzzy rules and applicable to automated assessment

Cited by 77 publications

References 29 publications

A Review on Text Summarization Techniques

A Review on Text Summarization Techniques

Automatic Keyword and Sentence-Based Text Summarization for Software Bug Reports

Abstract Text Summarization with a Convolutional Seq2seq Model

Contact Info

Product

Resources

About