Automatic Text Summarization Methods

Abstract

Withtheexponentialincreaseindigitalinformation, the challenge of information overload has become critical. Auto- matic Text Summarization (ATS) offers a solution by distilling keyinformationfromlargetextsintoconcisesummaries.Thispa- perexploresATSmethodologies,focusingonclassificationsbased on input type, purpose, and output type. It provides a detailed analysis of Extractive Text Summarization (ETS), Abstractive Text Summarization (ABS), and Hybrid Text Summarization (HTS). Our implemented ATS system achieves an impressive 90% accuracy, highlighting its effectiveness and reliability. By comparing techniques, datasets, and evaluation metrics, this paper identifies strengths and limitations while proposing future improvements in ATS systems.

Introduction

In today’s digital era, vast amounts of information create challenges of information overload, making it difficult for users to find relevant insights quickly. Text summarization addresses this by automatically condensing large texts into brief summaries that retain essential meaning, improving comprehension, productivity, and accessibility. It is crucial for various applications including news aggregation, healthcare, education, customer reviews, and legal document analysis.

Text summarization techniques are broadly categorized by input type (single-document or multi-document), purpose (indicative, informative, critical), and output type (extractive, abstractive, hybrid). Extractive summarization selects key sentences directly from the text, abstractive summarization paraphrases and generates new sentences, and hybrid methods combine both for improved quality.

Despite advancements in NLP and machine learning, challenges remain in understanding semantics, ensuring summary coherence, tailoring domain-specific systems, and evaluating summary quality. The paper highlights recent advances, such as transformer-based models like BERT, BART, and T5, and emphasizes the importance of these techniques for evolving natural language processing applications.

Conclusion

This paper provides a comprehensive overview of Auto- mated Text Summarization (ATS) techniques, categorizing them based on input, purpose, and output types. The study delvesintoExtractiveTextSummarization(ETS),Abstractive Text Summarization (ABS), and Hybrid Text Summarization (HTS), presenting detailed comparisons of their methodolo- gies,applications,andchallenges.ETS,implementedus- ing the TextRank algorithm in JavaScript, is efficient and straightforward but often lacks coherence. In contrast, ABS, leveragingthephilschmid/bart-large-cnn-samsum modelviaHuggingFace’s InferenceAPI, producesfluent and contextually accurate summaries but demands significant computationalresourcesandsuffersfromhallucinationissues. HTS emerges as a balanced solution, combining the factual correctness of ETS with the linguistic fluency of ABS, albeit with increased complexity and resource requirements. The implementation of HTS within a web-based frame- work using modern tools like React.js, Tailwind CSS, and TensorFlow.js demonstrated the effectiveness of combining extractionandabstraction,asevidencedbyimprovedROUGE scores.Theintegrationofdocumentpreprocessingtools (pdfjs-distandtesseract.js)furthershowcased the potential of ATS systems to handle diverse input formats.Thisstudyunderscorestheimportanceofselectingappropriate summarization approaches based on the application’s require- ments, available datasets, and computational resources. Despite advancements in ATS, challenges such as handling factual inconsistencies in ABS, improving semantic under- standing in ETS, and addressing scalability in HTS remain areas of active research. The findings emphasize the signifi- canceofcontinuousinnovationinATStechniquestomeetthe growing demand for automated solutions in an era dominated by information overload.

References

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Copyright

Copyright © 2025 Mayank Gangadhar Warkade, Devendra Gajanan Birari, Prof. Sakina Salmani. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.