Abstract
The rapid growth of digital text emphasises the necessity to create and build efficient summary tools to find and extract information in short form. Text overviews are typically handled using extraction techniques in natural language processing, which include choosing a portion of the original text to capture the core concept of the subject. Unlike the abstractive technique, it is utilised less than the extractive method, but outcomes are considerably closer to the human summary. In our study, we concentrate on the automated extractive kind of summary. In our study, we utilised Word2Vec, a collection of lexical integration models from Word Embedding. Word2Vec turns the word into a vector, and these segments are the derivatives of the word in our instance a 100 segment vector. We also utilised the CNN and auto-encoder. We also used the CNN. We use our model on Amazon Food Reviews datasets and assess rebuilt paragraphs using standard metrics (such as ROUGE). The aim of this research was to automatically generate the accurate and best summary.
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References
Gholamrezazadeh S, Salehi MA, Gholamzadeh B (2009) A comprehensive survey on text summarization systems. https://doi.org/10.1109/CSA.2009.5404226
Armano G, Giuliani A, Vargiu E (2011) Studying the impact of text summarization on contextual advertising. https://doi.org/10.1109/DEXA.2011.78
Afantenos S, Karkaletsis V, Stamatopoulos P (2005) Summarization from medical documents: a survey. Artif Intell Med 33(2). https://doi.org/10.1016/j.artmed.2004.07.017.
Lin CY (2004) Rouge: a package for automatic evaluation of summaries. In: Proceedings of the workshop on text summarization branches out (WAS 2004, 1)
Luhn HP (2010) The automatic creation of literature abstracts. IBM J Res Dev 2(2). https://doi.org/10.1147/rd.22.0159
Nenkova A, Passonneau R, McKeown K (2007) The pyramid method. ACM Trans Speech Lang Process 4(2). https://doi.org/10.1145/1233912.1233913
Kobayashi H, Noguchi M, Yatsuka T (2015) Summarization based on embedding distributions. https://doi.org/10.18653/v1/d15-1232
Chen L, le Nguyen M (2019) Sentence selective neural extractive summarization with reinforcement learning. https://doi.org/10.1109/KSE.2019.8919490
Chaganty AT, Mussmann S, Liang P (2018) The price of debiasing automatic metrics in natural language evaluation. In: ACL 2018—56th annual meeting of the Association for Computational Linguistics, proceedings of the conference (Long Papers), vol. 1. https://doi.org/10.18653/v1/p18-1060
Fan A, Grangier D, Auli M, Controllable abstractive summarization (2019). https://doi.org/10.18653/v1/w18-2706.
Böhm F, Gao Y, Meyer CM, Shapira O, Dagan I, Gurevych I (2020) Better rewards yield better summaries: learning to summarise without references. https://doi.org/10.18653/v1/d19-1307
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Boumahdi, F., Zeroual, A., Boulghiti, I., Hentabli, H. (2023). Generating an Extract Summary from a Document. In: Goyal, D., Kumar, A., Piuri, V., Paprzycki, M. (eds) Proceedings of the Third International Conference on Information Management and Machine Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-2065-3_13
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DOI: https://doi.org/10.1007/978-981-19-2065-3_13
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