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@article{159111,
        author = {Akash Ramkar and Yash Gaikwad and Bhavik Bedmutha and Dnyaneshwari Jogdand},
        title = {Document Text Summarization using Machine Learning and Natural Language Processing},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {11},
        pages = {333-335},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=159111},
        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.
},
        keywords = {document text summarization, machine learning, natural language processing, supervised learning, unsupervised learning, graph-based methods, deep learning, evaluation metrics, ROUGE, applications, ethics.},
        month = {},
        }