International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 08 | Aug 2023
p-ISSN: 2395-0072
www.irjet.net
Text Summarization Using the T5 Transformer Model Ratan Ravichandran1, Sri Bharath Sharma P2, Shriyans Shriniwas Arkal3, Shubhangee Das4, Prof. Sasikala Nagarajan5 1-5Department of Artificial Intelligence and Machine Learning, Dayananda Sagar University, Bangalore, India
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Abstract - In our information-filled world, it is crucial to
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focus on the essential content amidst the overwhelming volume of information available. Unfortunately, people often spend a significant amount of time sifting through irrelevant details, inadvertently overlooking crucial information. To address this issue, we present a project that utilizes the T5 transformer model in natural language processing to develop an abstractive text summarization system. By leveraging advanced language modeling techniques, our project aims to enhance efficiency, comprehension, and decision-making processes across various domains.
2. Literature Review Adhika Pramita Widyassari et al.[1] provides an overview of various techniques and methods used in automatic text summarization, with a particular focus on the Natural Language Toolkit (NLTK). The author explores different approaches, including extractive and abstractive summarization, and discusses how NLTK can be utilized in these techniques.
Key Words: Abstractive summarization, T5 transformer model, Natural language processing.
Preprocessing: NLTK performs essential text preprocessing tasks like tokenization, stemming, and stop-word removal, aiding in information extraction by breaking text into words or sentences and reducing words to their root form.
Sentence Scoring: NLTK facilitates extractive summarization by offering tools to calculate sentence similarity (e.g., cosine similarity) and assign scores, enabling the selection of relevant sentences based on their importance.
Feature Extraction: NLTK's part-of-speech tagging and named entity recognition assist in identifying entities and key terms, enhancing summary accuracy and relevance.
Language Modeling: In abstractive summarization, NLTK helps build language models (e.g., n-gram models) for generating concise and coherent summaries by predicting probable next words or phrases.
Evaluation: NLTK includes evaluation metrics (e.g., ROUGE, BLEU) to assess summary quality by comparing them with reference summaries and measuring similarity or effectiveness.
1.INTRODUCTION In our information-filled world, focusing on what truly matters is essential for success. On average, a person spends a significant amount of their lifetime reading useless information, often missing out on significant bits by subconsciously dismissing them. To solve this problem, we built a text summarizer that condenses lengthy text into shorter concise summaries, providing a quick overview of the main information. Text summarization is a vital tool in today's informationdriven world, allowing us to distil the essence of lengthy texts into concise summaries. By employing advanced natural language processing techniques, text summarizers extract key information, enabling readers to grasp the main ideas quickly. In this report, we explore the effectiveness and applications of text summarizers, shedding light on their potential to enhance efficiency, comprehension, and decision-making processes across various domains.
1.1 The T5 Transformer Model To achieve this, we use the T5 transformer model which is a powerful language model that can understand and generate human-like text. Constructing a text summarizer based on T5 is beneficial because it allows for concise and accurate summarization of lengthy documents. T5's ability to capture contextual relationships and generate coherent summaries makes it an ideal choice for text summarization tasks, enabling efficient information extraction and facilitating quick comprehension of complex texts.
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Khilji et al. [2] examines Abstractive Text Analysis, described as a natural language processing (NLP) technique that aims to generate a concise and coherent summary of a given text by understanding its content and generating new sentences. Abstractive summarization involves creating novel sentences that capture the key information and main ideas of the source text in a more human-like manner.
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