Trends in Extractive and Abstractive Techniques in Text Summarization

Bhatia, Neelima; Jaiswal, Arunima

doi:10.5120/20559-2947

Cited by 11 publications

(5 citation statements)

References 17 publications

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“…SimpleNLG is an example of such a system, which provides interfaces for direct control over the way phrases are created and merged and inflectional and morphological control. [250], [251] are examples of multimodal semantic methods utilized in text summarization.…”

Section: ) Multimodal Semantic Methodsmentioning

confidence: 99%

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

et al. 2021

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Section: ) Multimodal Semantic Methodsmentioning

confidence: 99%

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

et al. 2021

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“…In recent years, the task has been boosted by the development of deep learning algorithms and the explosive growth of data [3], especially for abstractive summarization. Large-scale high quality document-summary pairs are a precondition of high quality Table 1.…”

Section: Introductionmentioning

confidence: 99%

Identifying High Quality Document–Summary Pairs through Text Matching

Hou¹,

Xiang²,

Tang³

et al. 2017

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Text summarization namely, automatically generating a short summary of a given document, is a difficult task in natural language processing. Nowadays, deep learning as a new technique has gradually been deployed for text summarization, but there is still a lack of large-scale high quality datasets for this technique. In this paper, we proposed a novel deep learning method to identify high quality document-summary pairs for building a large-scale pairs dataset. Concretely, a long short-term memory (LSTM)-based model was designed to measure the quality of document-summary pairs. In order to leverage information across all parts of each document, we further proposed an improved LSTM-based model by removing the forget gate in the LSTM unit. Experiments conducted on the training set and the test set built upon Sina Weibo (a Chinese microblog website similar to Twitter) showed that the LSTM-based models significantly outperformed baseline models with regard to the area under receiver operating characteristic curve (AUC) value.

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“…Lee et al (2003) argue that a term which occurs more frequently is not necessarily a good discriminator, and should be given less weight than one which occurs less frequently. In order to overcome this problem, PCA has been extensively used in summarization tasks whereby a summary is generated by extracting sentences that are likely to represent the main theme of a document (Bhatia & Jaiswal, 2015;Canhasi & Kononenko;Kogilavani, 2016). This is one of the basic geometric tools that are used to produce a lower number of the vectors within a corpus (Härdle & Simar, 2003;Jackson, 1991).…”

Section: Previous Workmentioning

confidence: 99%

Addressing the Problem of Coherence in Automatic Text Summarization: A Latent Semantic Analysis Approach

Omar¹

2017

IJEL

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This article is concerned with addressing the problem of coherence in the automatic summarization of prose fiction texts. Despite the increasing advances within the summarization theory, applications and industry, many problems are still unresolved in relations to the applications of the summarization theory to literature. This can be in part attributed to the peculiar nature of literary texts where standard or typical summarization processes are not amenable for literature. This study, therefore, tends to bridge the gap between literature and summarization theory by proposing a summarization system that is based on more semantic-based approaches for extracting more meaningful and coherent summaries. Given that lack of coherence within summaries has its negative implications on understanding original texts; it follows that more effective methods should be developed in relation to the extraction of coherent summaries. In order to do this, a hybrid of methods including statistical (TF-IDF) and semantic (Latent Semantic Analysis LSA) methods were used to derive the most distinctive features and extract summaries from 10 English novellas. For evaluation purposes, both intrinsic and extrinsic methods are used for determining the quality of the extracted summaries. Results indicate that the integration of LSA into features extraction methods achieves better summarization performance outcomes in terms of coherence properties within the extracted summaries.

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Trends in Extractive and Abstractive Techniques in Text Summarization

Cited by 11 publications

References 17 publications

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

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

Identifying High Quality Document–Summary Pairs through Text Matching

Addressing the Problem of Coherence in Automatic Text Summarization: A Latent Semantic Analysis Approach

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