A Review on Text Summarization Techniques

Verma, Pradeepika; Verma, Anshul

doi:10.37398/jsr.2020.640148

Cited by 22 publications

(9 citation statements)

References 39 publications

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“…Then, each sentence is calculated to have a similarity value with the centroid based on cosine similarity, and then the feature values for each sentence are added together to get the sentence scores. A detailed review of techniques based on statistical approaches is discussed in [26] and [27].…”

Section: ) Statistical-based Methodsmentioning

confidence: 99%

A Comprehensive Review of Arabic Text Summarization

et al. 2022

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The explosion of online and offline data has changed how we gather, evaluate, and understand data. It is frequently difficult and time-consuming to comprehend large text documents and extract crucial information from them. Text summarization techniques address the mentioned problems by compressing long texts while retaining their essential contents. These techniques rely on the fast delivery of filtered, high-quality content to their users. Due to the massive amounts of data generated by technology and various sources, automated text summarization of large-scale data is challenging. There are three types of automatic text summarization techniques: extractive, abstractive, and hybrid. Regardless of these previous techniques, the generated summaries are a long way from the summarization produced by human experts. Although Arabic is a widely spoken language that is frequently used for content sharing on the web, Arabic text summarization of Arabic content is limited and still immature because of several problems, including the Arabic language's morphological structure, the variety of dialects, and the lack of adequate data sources. This paper reviews text summarization approaches and recent deep learning models for this approach. Additionally, it focuses on existing datasets for these approaches, which are also reviewed, along with their characteristics and limitations. The most often used metrics for summarization quality evaluation are ROUGE1, ROUGE2, ROUGE L, and Bleu. The challenges that are encountered during Arabic text summarizing methods and approaches and the solutions proposed in each approach are analyzed. Many Arabic text summarization methods have problems, such as the lack of golden tokens during testing, being out of vocabulary (OOV) words, repeating summary sentences, lack of standard systematic methodologies and architectures, and the complexity of the Arabic language. Finally, providing the required corpora, improving evaluation using semantic representations, the lack of using rouge metrics in abstractive text summarization, and using recent deep learning models to adopt them in Arabic summarization studies is an essential demand.

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Section: ) Statistical-based Methodsmentioning

confidence: 99%

A Comprehensive Review of Arabic Text Summarization

et al. 2022

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“…Various extractive and abstractive document summarization techniques have been proposed in literature based on evolutionary algorithms, neural networks, and clustering algorithms for single as well as multiple documents. Several recent surveys taking into account various effective models for text summarization on the basis of various parameters like relevancy, readability, redundancy, cohesion, coverage, precision, recall and accuracy are presented (Gambhir & Gupta, 2017;Lloret & Palomar, 2012;Nenkova & McKeown, 2012;Saggion & Poibeau, 2013;Torres-Moreno, 2014;Verma & Om, 2016;Verma & Verma, 2020a;Verma & Verma, 2020b). This section presents a brief survey of the most prominent and recent related works that contribute to the advancement of extractive document summarization for both single and multi-documents.…”

Section: Related Workmentioning

confidence: 99%

A fusion of variants of sentence scoring methods and collaborative word rankings for document summarization

Verma

Pal

2022

Expert Systems

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Document summarization is an important task in natural language processing that helps deal with the problem of information overload occurring due to the existence of redundant content. Summary generation with highly relevant contents and maximum coverage is particularly challenging which can only be achieved when redundancy is minimized. This article introduces a novel approach for automatic text summarization based on sentence scoring and collaborative ranking to produce summaries with minimal redundancy and improved overall performance of summarization. The proposed model is a fusion of weighted and unweighted features‐based sentence scoring methods. To learn optimal weights of text features, it has been modelled as an optimization problem. Moreover, the proposed model exploits the strength of collaborative ranking to generate the summary of a given document. Three similarity factors (proximity, significance and singularity)‐based models have been employed to find the similarity between weighted and unweighted sentence scores. The results of the comparison experiment demonstrate that the proposed (PS + Jac) method generates a closer summary to the reference summary with minimal redundant contents. On average, the proposed (PS + Jac) method generates the summaries with 61% accurate contents with greater improved rates up to 40%. The statistical testing also confirms that the performance improvement is significant at a 5% level of significance.

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“…They have observed that many summarization techniques suffer from various challenges and hence effective summarization techniques are required. [7] Existing systems: Sentence Scoring based on Word Frequency: The model initially assigns weight to each word of the given corpus. With the help of the weights assigned, the model will assign a score for each sentence.…”

Section: Literature Reviewmentioning

confidence: 99%

Text Summarization

Manju¹,

Radhamani²,

A³

et al. 2022

YMER

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n the last few years, a huge amount of text data from different sources has been created every day. The enormous data which needs to be processed contains valuable detail which needs to be efficiently summarized so that it serves a purpose. It is very tedious to summarize and classify large amounts of documents when done manually. It becomes cumbersome to develop a summary taking every semantics into consideration. Therefore, automatic text summarization acts as a solution. Text summarization can help in understanding the huge corpus by providing a gist of the corpus enabling comprehension in a timely manner. This paper studies the development of a web application which summarizes the given input text using different models and its deployment. Keywords: Text summarization, NLP, AWS, Text mining

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A Review on Text Summarization Techniques

Cited by 22 publications

References 39 publications

A Comprehensive Review of Arabic Text Summarization

A Comprehensive Review of Arabic Text Summarization

A fusion of variants of sentence scoring methods and collaborative word rankings for document summarization

Text Summarization

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