Document Text Summarization using Machine Learning and Natural Language Processing
Akash Ramkar, Yash Gaikwad, Bhavik Bedmutha, Dnyaneshwari Jogdand
document text summarization, machine learning, natural language processing, supervised learning, unsupervised learning, graph-based methods, deep learning, evaluation metrics, ROUGE, applications, ethics.
Document text summarization is a challenging task that aims to create a concise and informative summary of a longer document. In recent years, machine learning and natural language processing (NLP) techniques have been increasingly used for this task. This paper reviews the state-of-the-art techniques for document text summarization using machine learning and NLP. We begin by discussing the key challenges of document text summarization, including extractive and abstractive summarization, domain-specific summarization, and summarization of multimodal documents. This paper reviews the state-of-the-art techniques for document text summarization and discusses the challenges and approaches used in the field. The paper then presents a novel approach that combines supervised machine learning and graph-based methods to generate summaries, which outperforms existing methods on a benchmark dataset. Ethical considerations are also discussed, including the potential for biased or misleading summaries and the importance of transparency and explainability in summarization systems.
Article Details
Unique Paper ID: 159111

Publication Volume & Issue: Volume 9, Issue 11

Page(s): 333 - 335
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