提高TAC数据集上抽象多文档更新摘要的性能

2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) Pub Date : 2023-06-08 DOI:10.1109/HORA58378.2023.10156657

Marwa Khanom Nurtaj, Rafsan Bari Shafin, M. Hasan, Krittika Roy, M. S. Hossain Khan, Rashedul Amin Tuhin, Md. Mohsin Uddin

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引用次数: 0

摘要

在本文中，我们研究了各种前沿模型对TAC 2009数据集更新摘要的有效性。为了构建抽象和抽取的新闻摘要，我们使用了T5 Transformer模型和Textrank + Pegasus模型。我们的目标是评估这些模型如何从更新中捕获关键信息，并生成连贯和有用的摘要。在这里，我们使用常规的评估方法，如ROUGE来评估模型的性能。我们分析了从T5 Transformer模型、Textrank + Pegasus和TensorFlow模型生成的摘要的流畅性、连贯性和信息性，以及人工生成的黄金摘要。

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Enhancing Performance of Abstractive Multi-Document Update Summarization on TAC Dataset

In this paper, we investigate the efficacy of various cutting-edge models for update summarization on the TAC 2009 dataset. To construct abstractive and extractive summaries of news items, we use the T5 Transformer model and Textrank + Pegasus model. Our goal is to assess how well these models capture key information from updates and generate coherent and useful summaries. Here we use conventional assessment measures such as ROUGE to assess the performance of the models. We analyze the fluency, coherence, and informativeness of generated summaries from the T5 Transformer model, Textrank + Pegasus, and TensorFlow models against human-authored gold summaries.

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2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)

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