A Survey of Automatic Text Summarization: Progress, Process and Challenges

Mridha, M. F.; Lima, Aklima Akter; Nur, Kamruddin; Das, Sujoy Chandra; Hasan, Mahmud; Kabir, Md. Mohsin

doi:10.1109/access.2021.3129786

Cited by 65 publications

(31 citation statements)

References 240 publications

(261 reference statements)

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“…The intention of the last two columns is to provide an indication of the coverage of the work. The most complete surveys to date are the ones presented in El-Kassas et al (2021) and Mridha et al (2021). Like ours, they intend to cover most aspects of ATS systems.…”

Section: An Overview Of Other Ats Surveysmentioning

confidence: 99%

“…2001-2021 Sheik and Nirmala (2021) It presents an overview of deep learning for legal text summarization. 2004-2021 Mridha et al (2021) It presents a very general survey of extractive and full abstractive methods. It also presents a survey of available datasets used to investigate ATS systems and evaluation methods.…”

Section: Focusmentioning

confidence: 99%

“…Our paper naturally relates to other reviews about this theme. We may classify these reviews in terms of classical such as Edmundson and Wyllys (1961) and Paice (1990), topic-specific such as Rahman and Borah (2015) (query-based summarization), Pouriyeh et al (2018) (ontologybased summarization) and Alomari et al (2022) (deep learning approaches to summarization), and general reviews like ours such as Mridha et al (2021) and El-Kassas et al (2021). Although these later works are very related to ours in terms of general content, the presentation of our work is very different.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: An Overview Of Other Ats Surveysmentioning

confidence: 99%

Section: Focusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Cajueiro¹,

Nery²,

Tavares³

et al. 2023

Preprint

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show abstract

“…Abstractive summarization is an effective solution for summarizing these data. The primary research on text summarization began in the 1960s [2]. Summarization process can be chiefly categorized as extractive and abstractive [3] methods.…”

Section: Introductionmentioning

confidence: 99%

Ext-ICAS: A Novel Self-Normalized Extractive Intra Cosine Attention Similarity Summarization

Sharmila¹,

Deisy²,

Parthasarathy³

2023

Computer Systems Science and Engineering

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With the continuous growth of online news articles, there arises the necessity for an efficient abstractive summarization technique for the problem of information overloading. Abstractive summarization is highly complex and requires a deeper understanding and proper reasoning to come up with its own summary outline. Abstractive summarization task is framed as seq2seq modeling. Existing seq2seq methods perform better on short sequences; however, for long sequences, the performance degrades due to high computation and hence a two-phase self-normalized deep neural document summarization model consisting of improvised extractive cosine normalization and seq2seq abstractive phases has been proposed in this paper. The novelty is to parallelize the sequence computation training by incorporating feed-forward, the self-normalized neural network in the Extractive phase using Intra Cosine Attention Similarity (Ext-ICAS) with sentence dependency position. Also, it does not require any normalization technique explicitly. Our proposed abstractive Bidirectional Long Short Term Memory (Bi-LSTM) encoder sequence model performs better than the Bidirectional Gated Recurrent Unit (Bi-GRU) encoder with minimum training loss and with fast convergence. The proposed model was evaluated on the Cable News Network (CNN)/Daily Mail dataset and an average rouge score of 0.435 was achieved also computational training in the extractive phase was reduced by 59% with an average number of similarity computations.

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“…The purpose of summarizing is to produce a summary that contains the main content of a document in less space [1]. Automatic summarization helps users get the main idea of a document without having to read the whole document thus saving time and effort compared to doing it manually [2]. In general, automatic summarization can be categorized into two based on the approach in summarizing: extractive summarization and abstractive summarization.…”

Section: Introductionmentioning

confidence: 99%

Transformer-based Cross-Lingual Summarization using Multilingual Word Embeddings for English - Bahasa Indonesia

Abka¹,

Azizah²,

Jatmiko³

2022

IJACSA

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Cross-lingual summarization (CLS) is a process of generating a summary in the target language from a source document in another language. CLS is a challenging task because it involves two different languages. Traditionally, CLS is carried out in a pipeline scheme that involves two steps: summarization and translation. This approach has a problem, it introduces error propagation. To address this problem, we present a novel end-toend abstractive CLS without the explicit use of machine translation. The CLS architecture is based on Transformer which is proven to be able to perform text generation well. The CLS model is a jointly trained CLS task and monolingual summarization (MS) task. This is accomplished by adding a second decoder to handle the MS task, while the first decoder handles the CLS task. We also incorporated multilingual word embeddings (MWE) components into the architecture to further improve the performance of the CLS models. Both English and Bahasa Indonesia are represented by MWE whose embeddings have already been mapped into the same vector space. MWE helps to better map the relation between input and output that use different languages. Experiments show that the proposed model achieves improvement up to +0.2981 ROUGE-1, +0.2084 ROUGE-2, and +0.2771 ROUGE-L when compared to the pipeline baselines and up to +0.1288 ROUGE-1, +0.1185 ROUGE-2, and +0.1413 ROUGE-L when compared to the endto-end baselines.

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A Survey of Automatic Text Summarization: Progress, Process and Challenges

Cited by 65 publications

References 240 publications

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

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

Ext-ICAS: A Novel Self-Normalized Extractive Intra Cosine Attention Similarity Summarization

Transformer-based Cross-Lingual Summarization using Multilingual Word Embeddings for English - Bahasa Indonesia

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