A Comprehensive Survey on Extractive and Abstractive Techniques for Text Summarization

Mahajani, Abhishek; Pandya, Vinay; Maria, Isaac; Sharma, Deepak

doi:10.1007/978-981-13-5934-7_31

Cited by 31 publications

(15 citation statements)

References 10 publications

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“…Some studies presented and discussed the analysis of some abstractive and extractive approaches. These studies included details on abstract and extractive on resume assessment methods [24,25].…”

Section: Related Workmentioning

confidence: 99%

BiDETS: Binary Differential Evolutionary based Text Summarization

Aljahdali¹,

Ahmed²,

Abuobieda³

2021

IJACSA

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In extraction-based automatic text summarization (ATS) applications, feature scoring is the cornerstone of the summarization process since it is used for selecting the candidate summary sentences. Handling all features equally leads to generating disqualified summaries. Feature Weighting (FW) is an important approach used to weight the scores of the features based on their presence importance in the current context. Therefore, some of the ATS researchers have proposed evolutionary-based machine learning methods, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), to extract superior weights to their assigned features. Then the extracted weights are used to tune the scored-features in order to generate a high qualified summary. In this paper, the Differential Evolution (DE) algorithm was proposed to act as a feature weighting machine learning method for extraction-based ATS problems. In addition to enabling the DE to represent and control the assigned features in binary dimension space, it was modulated into a binary coded format. Simple mathematical calculation features have been selected from various literature and employed in this study. The sentences in the documents are first clustered according to a multi-objective clustering concept. DE approach simultaneously optimizes two objective functions, which are compactness measuring and separating the sentence clusters based on these objectives. In order to automatically detect a number of sentence clusters contained in a document, representative sentences from various clusters are chosen with certain sentence scoring features to produce the summary. The method was tested and trained using DUC2002 dataset to learn the weight of each feature. To create comparative and competitive findings, the proposed DE method was compared with evolutionary methods: PSO and GA. The DE was also compared against the best and worst systems benchmark in DUC 2002. The performance of the BiDETS model is scored with 49% similar to human performance (52%) in ROUGE-1; 26% which is over the human performance (23%) using ROUGE-2; and lastly 45% similar to human performance (48%) using ROUGE-L. These results showed that the proposed method outperformed all other methods in terms of F-measure using the ROUGE evaluation tool.

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Section: Related Workmentioning

confidence: 99%

BiDETS: Binary Differential Evolutionary based Text Summarization

Aljahdali¹,

Ahmed²,

Abuobieda³

2021

IJACSA

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“…However, there are no comparisons of the quality of several models that generated summaries. Furthermore, both extractive and abstractive summarisation models were summarised in [20,21]. In [20], the classification of summarisation tasks was based on three factors: input factors, purpose factors, and output factors.…”

Section: Introductionmentioning

confidence: 99%

“…Dong and Mahajani et al surveyed only five abstractive summarisation models each. On the other hand, Mahajani et al focused on the datasets and training techniques in addition to the architecture of several abstractive summarisation models [21]. However, the quality of the generated summary of the different techniques and the evaluation measures were not discussed.…”

Section: Introductionmentioning

confidence: 99%

“…Although several papers have analysed abstractive summarisation models, few papers have performed a comprehensive study [23]. Moreover, most of the previous surveys covered the techniques until 2018, even though surveys were published in 2019 and 2020, such as [20,21]. In this review, we addressed most of the recent deep learning-based RNN abstractive text summarisation models.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Based Abstractive Text Summarization: Approaches, Datasets, Evaluation Measures, and Challenges

Suleiman

Awajan

2020

Mathematical Problems in Engineering

101

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In recent years, the volume of textual data has rapidly increased, which has generated a valuable resource for extracting and analysing information. To retrieve useful knowledge within a reasonable time period, this information must be summarised. This paper reviews recent approaches for abstractive text summarisation using deep learning models. In addition, existing datasets for training and validating these approaches are reviewed, and their features and limitations are presented. The Gigaword dataset is commonly employed for single-sentence summary approaches, while the Cable News Network (CNN)/Daily Mail dataset is commonly employed for multisentence summary approaches. Furthermore, the measures that are utilised to evaluate the quality of summarisation are investigated, and Recall-Oriented Understudy for Gisting Evaluation 1 (ROUGE1), ROUGE2, and ROUGE-L are determined to be the most commonly applied metrics. The challenges that are encountered during the summarisation process and the solutions proposed in each approach are analysed. The analysis of the several approaches shows that recurrent neural networks with an attention mechanism and long short-term memory (LSTM) are the most prevalent techniques for abstractive text summarisation. The experimental results show that text summarisation with a pretrained encoder model achieved the highest values for ROUGE1, ROUGE2, and ROUGE-L (43.85, 20.34, and 39.9, respectively). Furthermore, it was determined that most abstractive text summarisation models faced challenges such as the unavailability of a golden token at testing time, out-of-vocabulary (OOV) words, summary sentence repetition, inaccurate sentences, and fake facts.

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“…Existing methods use the information contained in the analyzed document based on natural language processing (NLP) or statistical techniques. A more detailed survey of techniques used for text documents summarization is provided in [42]. In general, there are two approaches: (1) Extraction -it recognizes a subset of terms or phrases in the original document to create a summary; (2) Abstraction -it constructs an internal semantic representation using natural language generation techniques to create a synthesis closer to what a human could generate.…”

Section: Introductionmentioning

confidence: 99%

Using a multimedia semantic graph for web document visualization and summarization

Rinaldi

Russo

2020

Multimed Tools Appl

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The synthesis process of document content and its visualization play a basic role in the context of knowledge representation and retrieval. Existing methods for tag-clouds generations are mostly based on text content of documents, others also consider statistical or semantic information to enrich the document summary, while precious information deriving from multimedia content is often neglected. In this paper we present a document summarization and visualization technique based on both statistical and semantic analysis of textual and visual contents. The result of our framework is a Visual Semantic Tag Cloud based on the highlighting of relevant terms in a document using some features (font size, color, etc.) showing the importance of a term compared to other ones. The semantic information is derived from a knowledge base where concepts are represented through several multimedia items. The Visual Semantic Tag Cloud can be used not only to synthesize a document but also to represent a set of documents grouped by categories using a topic detection technique based on textual and visual analysis of multimedia features. Our work aims at demonstrating that with the help of semantic analysis and the combination of textual and visual features it is possible to improve the user knowledge acquisition by means of a synthesized visualization. The whole strategy has been evaluated by means of a ground truth and compared with similar approaches. Experimental results show the effectiveness of our approach, which outperforms state-of-art algorithms in topic detection combining both visual and semantic information.

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A Comprehensive Survey on Extractive and Abstractive Techniques for Text Summarization

Cited by 31 publications

References 10 publications

BiDETS: Binary Differential Evolutionary based Text Summarization

BiDETS: Binary Differential Evolutionary based Text Summarization

Deep Learning Based Abstractive Text Summarization: Approaches, Datasets, Evaluation Measures, and Challenges

Using a multimedia semantic graph for web document visualization and summarization

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