Automated Text Summarization: A Comprehensive Review

Automated Text Summarization: A Comprehensive Review

Authors

Keywords:

Automated document summarization, extractive approach, abstractive method, literature review

Abstract

The contemporary era is characterized by an excess of information, wherein data pertaining to a single subject requires considerable time for manual condensation due to its extensive volume. To tackle this challenge, methodologies for Automated Text Summarization have been devised. Presently, two predominant techniques are employed: Extractive and Abstractive methods. This study conducts a comprehensive review of publications within the IEEE and ACM libraries that focus on Automated Text Summarization in the English language.

References

Afsharizadeh, M., Ebrahimpour-Komleh, H., Bagheri, A. (2018). Query-oriented text summarization using sentence extraction technique. 2018 4th International Conference on Web Research (ICWR), IEEE, pp. 128–132.

Bahdanau, D., Cho, K., Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate.

Chauhan, K. (2018). Unsupervised text summarization using sentence embeddings.

Day, M. Y., Chen, C. Y. (2018). Artificial intelligence for automatic text summarization. 2018 IEEE International Conference on Information Reuse and Integration (IRI), pp. 478–484.

de la Pena Sarrac ˜ en, G. L., Rosso, P. ´ (2018). Automatic text summarization based on betweenness centrality. Proceedings of the 5th Spanish Conference on Information Retrieval, pp. 11.

Ferreira, R., Freitas, F., Cabral, L. d. S., Lins, R. D., Lima, R., Franc¸ a, G., Simskez, S. J., Favaro, L. (2013). A four-dimension graph model for automatic text summarization. 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), volume 1, pp. 389–396.

Hamid, F., Tarau, P. (2014). Text summarization as an assistive technology. Proceedings of the 7th International Conference on Pervasive Technologies Related to Assistive Environments, Association for Computing Machinery, pp. 60.

Hingu, D., Shah, D., Udmale, S. (2015). Automatic text summarization of Wikipedia articles view document. 2015 International Conference on Communication, Information & Computing Technology, IEEE, pp. 1–4.

Krishnaveni, P., Balasundaram, S. R. (2017). Automatic text summarization by local scoring and ranking for improving coherence. 2017 International Conference on Computing Methodologies and Communication, pp. 59–64.

Lin, C. Y. (2004). ROUGE: A package for automatic evaluation of summaries. Text Summarization Branches Out, Association for Computational Linguistics, pp. 74–81.

Mani, I., Maybury, M. T. (1999). Advances in automatic text summarization. MIT press.

Nallapati, R., Zhou, B., dos Santos, C. N., Gulcehre, C., Xiang, B. (2016). Abstractive text summarization using sequence-to-sequence RNNs and beyond.

NIST (2018). Text Analysis Conference.

RxNLP (2018). ROUGE Evaluation Metrics.

Sethi, P., Sonawane, S., Khanwalker, S., Keskar, R. B. (2017). Automatic text summarization of news articles. 2017 International Conference on Big Data, IoT and Data Science (BID), pp. 23–29.

Softsonic (2018). List of Online Automatic Test Summarization Tools.

Text Summarizer (2018). Manual of Text Summarization.

Voorhees, E. (2002). Document understanding conferences website.

Downloads

Published

2023-09-22

How to Cite

Turner, L. (2023). Automated Text Summarization: A Comprehensive Review. Infotech Journal Scientific and Academic , 4(2), 18–34. Retrieved from https://infotechjournal.org/index.php/infotech/article/view/29

Issue

Section

Articles
Loading...