[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117420-en":3,"doc-seo-117420-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117420,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Enhancing News Article Summarization with Machine Learning - Volume 3 - Issue 4","The exponential growth of web-based articles drives an urgent need for automated, efficient information processing and summarization. A machine learning model is proposed to generate coherent and concise summaries, emphasizing text preprocessing steps including tokenization, stopword removal, and stemming. The approach incorporates feature extraction, model training, and integration support using libraries such as NLTK and TensorFlow. Baseline models are described for testing and evaluation, while performance results demonstrate both quality and efficiency. Key challenges in handling complex language and contextual information are discussed alongside directions for future improvements.","Enhancing News Article Summarization with Machine Learning  \nAnand Prakash  \n(IJGASR) International Journal For Global Academic & Scientific Research ISSN Number: 2583-3081  \nVolume 3, Issue No. 4, 20–34 © The Author 2024  \n[journals.icapsr.com/index.php/ijgasr](journals.icapsr.com/index.php/ijgasr)  \n[DOI: 10.55938_ijgasr.v3i4.152](DOI: 10.55938_ijgasr.v3i4.152)  \nAbstract  \nThe exponential growth in web-based articles necessitates an immediate need for automated tools for information processing and summarization in an efficient manner. In this work, a model for generating summaries of articles using a machine learning approach is proposed, with a strong focus placedon techniques generating coherent and concise summaries. Text preprocessing techniques such astokenization, stopword removal, and stemming are part of the proposed model, followed by feature extraction and model training with machine learning platforms. Libraries such as NLTK and TensorFlow are leveraged for supporting processing of text and model integration for summarization. Baseline models for testing and evaluation are proposed, and performance of the proposed model in generating highquality summaries with efficiency is proven through demonstration. Challenges in processing complex language and contextual information are discussed, and future work in overcoming such weaknesses and performance improvement in summarization is discussed in detail in the work. In conclusion, this work is a contribution to the new field of automated article summarization, providing a feasible, efficient, and effective model for use in practice. It identifies and advocates for use of machine learning for changing consumption and processing of articles, and it is a useful contribution for developing such a field in practice.  \nKeywords  \nNews Summarization, Machine Learning, Python, Automated Summarization, Feature Extraction Received: 15 October 2024; Revised: 20 December 2024; Accepted: 05 January 2025;  \nPublished: 08 January 2025 Introduction  \nText summarization entails producing concise, readable, and correct summaries of long text documents. With current times, such a long and ever-growing collection of web-based articles in newspapers and newswires is a significant challenge for interested readers in getting to know current events in a timely  \nKelly School of Business, Indiana University, [anandintouch@gmail.com](anandintouch@gmail.com)  \nCorresponding Author:  \nEmail-id: [anandintouch@gmail.com](anandintouch@gmail.com)  \n© 2024 by Anand Prakash Submitted for possible open access publication under the terms and conditions of the  \nCreative Commons Attribution (CC BY) license,([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)). This work is licensed under a Creative Commons Attribution 4.0 International License  \nmanner. Methods of summarization try to counteract such an issue through readable, concise renditions of long articles, such that one can understand important information at a glance, no reading of the full article being necessitated [1] .  \nSummarization methods can broadly fall under two categories: abstractive and extractive. Abstractivesummarization involves analysis of a document's important concepts and rewording such concepts incoherent, natural language form. Abstractive summarization imitates human summarization, and therefore, a deeper level of understanding in producing summaries with new sentences and phrases not present in the source is involved [2] . On the other hand, extractive summarization involves searching and selecting important sentences, phrases, or paragraphs in direct form in the source to produce a summary. Extractive techniques involve computationally less complex operations, utilizing ranking and concatenation of present contents in contrast to producing new contents [3] .  \nThis research addresses a machine learning-based approach towards article summarization, with a target of generating concise ","cbCaieAXsHpMu6q6","https://ap.wps.com/l/cbCaieAXsHpMu6q6","pdf",558577,1,15,"English","en",105,"# Introduction\n## Summarization methods: Abstractive vs Extractive\n## Proposed machine learning approach\n## Key summarization steps\n## Model pipeline and techniques","[{\"question\":\"What problem does the paper address in news summarization?\",\"answer\":\"It addresses the challenge created by the rapid growth of web-based news articles, which makes timely understanding difficult for readers without automated summarization tools.\"},{\"question\":\"What are the main types of summarization discussed?\",\"answer\":\"The paper distinguishes abstractive summarization (rephrasing key concepts into new sentences) from extractive summarization (selecting and combining existing sentences, phrases, or paragraphs).\"},{\"question\":\"Which techniques are used in the proposed machine learning model?\",\"answer\":\"The model uses text preprocessing (tokenization, stopword removal, stemming), feature extraction methods such as TF-IDF and word embeddings, and high-performance architectures including LSTM and Transformer networks.\"}]","Enhancing News Article Summarization with Machine Learning - 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