Review of automatic text summarization techniques &amp; methods

Widyassari, Adhika Pramita; Rustad, Supriadi; Shidik, Guruh Fajar; Noersasongko, Edi; Syukur, Abdul; Affandy, Affandy; Setiadi, De Rosal Ignatius Moses

doi:10.1016/j.jksuci.2020.05.006

Cited by 130 publications

(76 citation statements)

References 76 publications

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“…Extractive methods crop out one or more segments from the input text and concatenate them to produce a summary. These methods were dominant in the early era of summarization, but they suffer from some limitations, including weak coherence between sentences, inability to simplify complex and long sentences, and unintended repetition (See et al, 2017;Widyassari et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages

Hasan¹,

Bhattacharjee²,

Islam³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Contemporary works on abstractive text summarization have focused primarily on highresource languages like English, mostly due to the limited availability of datasets for low/midresource ones. In this work, we present XL-Sum, a comprehensive and diverse dataset comprising 1 million professionally annotated article-summary pairs from BBC, extracted using a set of carefully designed heuristics. The dataset covers 44 languages ranging from low to high-resource, for many of which no public dataset is currently available. XL-Sum is highly abstractive, concise, and of high quality, as indicated by human and intrinsic evaluation. We fine-tune mT5, a state-of-theart pretrained multilingual model, with XL-Sum and experiment on multilingual and lowresource summarization tasks. XL-Sum induces competitive results compared to the ones obtained using similar monolingual datasets: we show higher than 11 ROUGE-2 scores on 10 languages we benchmark on, with some of them exceeding 15, as obtained by multilingual training. Additionally, training on low-resource languages individually also provides competitive performance. To the best of our knowledge, XL-Sum is the largest abstractive summarization dataset in terms of the number of samples collected from a single source and the number of languages covered. We are releasing our dataset and models to encourage future research on multilingual abstractive summarization. The resources can be found at https://github. com/csebuetnlp/xl-sum.

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Section: Discussionmentioning

confidence: 99%

XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages

Hasan¹,

Bhattacharjee²,

Islam³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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“…The inversion operation extracts all pairs of items that are out of order: I NV(π) = {(a i , a j )|i < j ∧ a i > a j }, where π is a list of comparable elements [36]. For instance, with the list [1,5,4,2], the inverse operation results in [(5, 4), (5,2), (4,2)].…”

Section: Metricsmentioning

confidence: 99%

“…[(1, 11), (7,13), (21,4), (24,7), (28,1)] The resulting list of the INV operation applied on the list made up with the second components of the pairs of the previous list is: (11,4), (11,7), (11,1), (13,4), (13,7), (13, 1), (4, 1), (7, 1)] The Reorder(T, S) operation is 4 since there are 4 extractive reordered segments. This value is computed as the unique values in the first components of the pairs in the previous list (11,13,4,7). Additionally, the length (in words) of the summary is 14, there are 5 extractive fragments, and the sum of their length is 13.…”

Section: And the Following Summary (S)mentioning

confidence: 99%

“…In this way, summaries are not mere clippings of the original documents; rather, abstractive summarizations are created by choosing the most important phrases of the documents and paraphrasing that content, creating a combination of some phrases, introducing new words, searching for synonyms, creating generalizations or specifications of some words or reordering content. All these actions must be done while preserving the linguistic cohesion and the coherence of the information [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish

et al. 2021

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Most of the models proposed in the literature for abstractive summarization are generally suitable for the English language but not for other languages. Multilingual models were introduced to address that language constraint, but despite their applicability being broader than that of the monolingual models, their performance is typically lower, especially for minority languages like Catalan. In this paper, we present a monolingual model for abstractive summarization of textual content in the Catalan language. The model is a Transformer encoder-decoder which is pretrained and fine-tuned specifically for the Catalan language using a corpus of newspaper articles. In the pretraining phase, we introduced several self-supervised tasks to specialize the model on the summarization task and to increase the abstractivity of the generated summaries. To study the performance of our proposal in languages with higher resources than Catalan, we replicate the model and the experimentation for the Spanish language. The usual evaluation metrics, not only the most used ROUGE measure but also other more semantic ones such as BertScore, do not allow to correctly evaluate the abstractivity of the generated summaries. In this work, we also present a new metric, called content reordering, to evaluate one of the most common characteristics of abstractive summaries, the rearrangement of the original content. We carried out an exhaustive experimentation to compare the performance of the monolingual models proposed in this work with two of the most widely used multilingual models in text summarization, mBART and mT5. The experimentation results support the quality of our monolingual models, especially considering that the multilingual models were pretrained with many more resources than those used in our models. Likewise, it is shown that the pretraining tasks helped to increase the degree of abstractivity of the generated summaries. To our knowledge, this is the first work that explores a monolingual approach for abstractive summarization both in Catalan and Spanish.

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“…As the most classical text sentiment analysis method, the sentiment dictionary method has the characteristics of simple operation, but the method is limited in the establishment of sentiment dictionary, thesaurus scope, accuracy, and other aspects [8]. Traditional machine learning methods, such as the K-nearest neighbor algorithm (KNN), decision tree (DT), and support vector machine (SVM), can ensure the accuracy of sentiment classification to a certain extent [9], but most of the above methods rely on manually labeled training sets and features extracted by established methods. The accuracy of training set labeling and the effectiveness of features largely determine the accuracy of traditional machine learning methods.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese

Yan

et al. 2021

Applied Sciences

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In recent years, online course learning has gradually become the mainstream of learning. As the key data reflecting the quality of online courses, users’ comments are very important for improving the quality of online courses. The sentiment information contained in comments is the guide of course improvement. A new ensemble model is proposed for sentiment analysis. The model takes full advantage of Word2Vec and Glove in word vector representation, and utilizes the bidirectional long and short time network and convolutional neural network to achieve deep feature extraction. Moreover, the multi-objective gray wolf optimization (MOGWO) ensemble method is adopted to integrate the models mentioned above. The experimental results show that the sentiment recognition accuracy of the proposed model is higher than that of the other seven comparison models, with an F1score over 91%, and the recognition results of different emotion levels indicate the stability of the proposed ensemble model.

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Review of automatic text summarization techniques & methods

Cited by 130 publications

References 76 publications

XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages

XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages

NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish

Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese

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