Document Text Summarization using Machine Learning and Natural Language Processing
Author(s):
Akash Ramkar, Yash Gaikwad, Bhavik Bedmutha, Dnyaneshwari Jogdand
Keywords:
document text summarization, machine learning, natural language processing, supervised learning, unsupervised learning, graph-based methods, deep learning, evaluation metrics, ROUGE, applications, ethics.
Abstract
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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