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Link to original content: https://unpaywall.org/10.1007/S11042-024-18248-2
A deep learning framework for multi-document summarization using LSTM with improved Dingo Optimizer (IDO) | Multimedia Tools and Applications Skip to main content
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A deep learning framework for multi-document summarization using LSTM with improved Dingo Optimizer (IDO)

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Abstract

Multi-document summarization (MDS) is a topic of much attention in extensive knowledge areas. Extractive MDS techniques intend to shrink the text from a document compilation by enclosing essential content and minimizing unnecessary data. MDS is more challenging than single document summarization and has several weaknesses, including an inaccurate selection of important sentences, a percentage of low coverage, and redundancy among the sentences. To address these issues, our proposed system focuses on pioneering an innovative automated extractive MDS approach. The process begins with original document pre-processing, followed by the extraction of features such as modified TF-IDF, Bag of Word (BOW), and concept similarity (CS) features. These features are then inputted into a Long Short-Term Memory (LSTM) framework. The model's weights are fine-tuned using the Improved Dingo Optimization (IDO) technique. The proposed model is evaluated on the Amazon Review and DUC-2002 datasets and compared its performance with various existing algorithms. The results demonstrated significant enhancements over baseline models, with an accuracy of 0.922862 for the Amazon Review dataset and 0.899730 for the DUC2002 dataset. These findings underscore the effectiveness of our developed technique in improving the accuracy of extractive multi-document summarization.

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Data availability

https://www.kaggle.com/currie32/summarizing-text-with-amazon-reviews/data

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Authors and Affiliations

  1. Department of Computer Science & Engineering, MNIT Jaipur, Malviya Nagar, Jaipur, Rajasthan, 302017, India

    Geetanjali Singh, Namita Mittal & Satyendra Singh Chouhan

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  1. Geetanjali Singh
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  2. Namita Mittal
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  3. Satyendra Singh Chouhan
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Singh, G., Mittal, N. & Chouhan, S.S. A deep learning framework for multi-document summarization using LSTM with improved Dingo Optimizer (IDO). Multimed Tools Appl 83, 69669–69691 (2024). https://doi.org/10.1007/s11042-024-18248-2

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