Abstract
Text summarization has become very essential tool to record important points and has been used by several websites and applications to lessen length, difficulty, and to preserve the vital information of the original file. The requirement on well-organized and useful text summarization of the website content, news feed and other kinds of legal documents with judgments and predilection is the demand of the present requirement. Hence several attempts have been made to automate the summarizing process. The recent development and state of the art models in natural language processing demonstrated outstanding results in text summarization, however major focus of these analysis was on large dataset with large parameters. This study’s primary purpose is to evaluate the performance of ensemble abstractive and extractive models on text summarization. Combined core of BERT and PEGASUS models’ output were applied to LexRank model on News Summary dataset to evaluate the performance through ROUGE metric. The results showed the performance of combined and ensemble model is better than individual performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
See, A., Liu, P.J., Manning, C.D.: Get to the point: summarization with pointer-generator networks. arXiv preprint arXiv:1704.04368 (2017)
Aksenov, D., Julián, M., Peter, B., Robert, S., Leonhard, H., Georg, R.: Abstractive text summarization based on language model conditioning and locality modeling. arXiv arXiv:2003.13027 (2020)
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI Blog 1(8), 9 (2019)
Fabbri, A.R., Kryściński, W., McCann, B., Xiong, C., Socher, R., Radev, D.: Summeval: re-evaluating summarization evaluation. Trans. Assoc. Comput. Linguist. 9, 391–409 (2021)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014)
Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)
Agarwal, A., Lavie, A.: METEOR: an automatic metric for MT evaluation with high levels of correlation with human judgments. In: Proceedings of WMT-08 (2007)
Tjandra, A., Sakti, S., Nakamura, S.: Multi-scale alignment and contextual history for attention mechanism in sequence-to-sequence model. In: 2018 IEEE Spoken Language Technology Workshop (SLT), pp. 648–655. IEEE (2018)
Cohan, A., Goharian, N.: Revisiting summarization evaluation for scientific articles. arXiv preprint arXiv:1604.00400 (2016)
Zhang, J., Zhao, Y., Saleh, M., Liu, P.: PEGASUS: pre-training with extracted gap-sentences for abstractive summarization. In: International Conference on Machine Learning, pp. 11328–11339. PMLR (2020)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Celikyilmaz, A., Bosselut, A., He, X., Choi, Y.: Deep communicating agents for abstractive summarization. arXiv preprint arXiv:1803.10357 (2018)
Bouscarrat, L., Bonnefoy, A., Peel, T., Pereira, C.: STRASS: a light and effective method for extractive summarization based on sentence embeddings. arXiv preprint arXiv:1907.07323 (2019)
Hao, Y., Dong, L., Wei, F., Xu, K.: Visualizing and understanding the effectiveness of BERT. arXiv preprint arXiv:1908.05620 (2019)
Erkan, G., Radev, D.R.: LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457–479 (2004)
Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004)
Kaggle (2022). https://www.kaggle.com/
Goodwin, T.R., Savery, M.E., Demner-Fushman, D.: Flight of the PEGASUS? Comparing transformers on few-shot and zero-shot multi-document abstractive summarization. In: Proceedings of COLING. International Conference on Computational Linguistics, vol. 2020, p. 5640. NIH Public Access (2020)
Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)
Liu, Y.: Fine-tune BERT for extractive summarization. arXiv preprint arXiv:1903.10318 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lenka, R.K.B. et al. (2023). Evaluation of Extractive and Abstract Methods in Text Summarization. In: Wah, Y.B., Berry, M.W., Mohamed, A., Al-Jumeily, D. (eds) Data Science and Emerging Technologies. DaSET 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-99-0741-0_38
Download citation
DOI: https://doi.org/10.1007/978-981-99-0741-0_38
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0740-3
Online ISBN: 978-981-99-0741-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)