Text Summarization Using Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI Chat-GPT Models
(2310.10449)Abstract
Text summarization is a critical NLP task with applications ranging from information retrieval to content generation. Leveraging LLMs has shown remarkable promise in enhancing summarization techniques. This paper embarks on an exploration of text summarization with a diverse set of LLMs, including MPT-7b-instruct, falcon-7b-instruct, and OpenAI ChatGPT text-davinci-003 models. The experiment was performed with different hyperparameters and evaluated the generated summaries using widely accepted metrics such as the Bilingual Evaluation Understudy (BLEU) Score, Recall-Oriented Understudy for Gisting Evaluation (ROUGE) Score, and Bidirectional Encoder Representations from Transformers (BERT) Score. According to the experiment, text-davinci-003 outperformed the others. This investigation involved two distinct datasets: CNN Daily Mail and XSum. Its primary objective was to provide a comprehensive understanding of the performance of LLMs when applied to different datasets. The assessment of these models' effectiveness contributes valuable insights to researchers and practitioners within the NLP domain. This work serves as a resource for those interested in harnessing the potential of LLMs for text summarization and lays the foundation for the development of advanced Generative AI applications aimed at addressing a wide spectrum of business challenges.
Overview
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This paper provides an in-depth comparative analysis of three LLMs for text summarization: MPT-7b-instruct, Falcon-7b-instruct, and OpenAI's Chat-GPT (text-davinci-003), highlighting their performance on different datasets.
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The study explores both abstractive and extractive summarization methods, detailing the differentiation between supervised and unsupervised approaches in the context of LLM-based text summarization.
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Utilizing CNN/Daily Mail 3.0.0 and Extreme Summarization (XSum) as evaluation datasets, the paper assesses model performance using BLEU Score, ROUGE Score, and BERT Score metrics.
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OpenAI's text-davinci-003 model outperforms the others, demonstrating superior summary generation capabilities, with future research suggested on domain-specific fine-tuning and models with larger parameters.
Comparative Evaluation of LLMs in Text Summarization: Insights from a Rigorous Study
Introduction to LLM-based Text Summarization
In the current digital age, the ability to condense extensive information into concise summaries is invaluable, necessitating sophisticated NLP techniques. Among these, text summarization using LLMs has emerged as a prominent method. The paper under discussion provides a meticulous comparative analysis of three LLMs: MPT-7b-instruct, Falcon-7b-instruct, and OpenAI's Chat-GPT (text-davinci-003), specifically in the context of text summarization. The study utilizes a range of hyperparameters and evaluates the generated summaries using established metrics, revealing insights into the capabilities and potential applications of these models in text summarization tasks.
Overview of LLMs and Text Summarization Methods
The study outlines two principal text summarization methods: abstractive and extractive summarization. Abstractive summarization entails rewriting key information in a new form, often requiring a deep understanding of the content, while extractive summarization involves selecting pertinent phrases or sentences from the original text. Furthermore, the paper differentiates between supervised and unsupervised summarization approaches, highlighting the dependency of the former on labeled data for model training.
Experimental Dataset and Metrics
The comparative analysis utilized two datasets: CNN/Daily Mail 3.0.0 and Extreme Summarization (XSum). These datasets, comprising news articles with associated summaries, allowed for a robust evaluation of the LLMs against various content types. The study employed several evaluation metrics, namely BLEU Score, ROUGE Score, and BERT Score, to quantitatively assess the quality of summaries generated by each model.
Findings and Model Performance
The performance analysis revealed that OpenAI's text-davinci-003 consistently outperformed the other models across both datasets. This model demonstrated superior abilities in generating high-quality summaries, as evidenced by its scores in BLEU, ROUGE, and BERT metrics. Notably, while MPT-7b-instruct slightly outshined Falcon-7b-instruct, the two models displayed comparable capabilities in certain aspects of text summarization.
Implications and Future Directions
This study underscores the profound impact of model architecture and size on the efficiency of text summarization tasks. It highlights the particularly promising utility of OpenAI’s model in achieving advanced results in various NLP applications. Looking ahead, expanding this research to investigate models with larger parameters and exploring domain-specific fine-tuning could further refine summarization performance and open new avenues in generative AI applications.
Acknowledgements
The research benefited greatly from the support and mentorship within the KaggleX BIPOC Mentorship Program, emphasizing the significance of collaborative efforts and access to computational resources in advancing NLP research.
Conclusion
This paper offers comprehensive insights into the application of LLMs for text summarization, presenting a detailed comparative analysis that elucidates the strengths and potential areas for improvement of different models. As the field of NLP continues to evolve, leveraging the capabilities of sophisticated models like OpenAI's text-davinci-003 will be crucial in addressing complex summarization tasks and furthering the development of generative AI technologies.
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