A Survey of Text Summarization Techniques for Indian Regional Languages

Shimpikar, Sheetal; Govilkar, Sharvari

doi:10.5120/ijca2017914083

Cited by 9 publications

(1 citation statement)

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“…Structured approaches encode the crucial features of documents using several types of schemas, including tree, ontology, lead and body phrases, and template and rule-based schemas, while semantic-based approaches are more concerned with the semantics of the text and thus rely on the information representation of the document to summarise the text. Semantic-based approaches include the multimodal semantic method, information item method, and semantic graph-based method [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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

Suleiman

Awajan

2020

Mathematical Problems in Engineering

103

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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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