Fine-tuning the Natural Language Processing (NLP) models for each new data set requires higher computational time associated with increased carbon footprint and cost. However, fine-tuning helps the pre-trained models adapt to the latest data sets; what if we avoid the fine-tuning steps and attempt to generate summaries using just the pre-trained models to reduce computational time and cost. In this paper, we tried to omit the fine-tuning steps and investigate whether the Marginal Maximum Relevance (MMR)-based approach can help the pre-trained models to obtain query-focused summaries directly from a new data set that was not used to pre-train the models. First, we used topic modelling on Wikipedia Current Events Portal (WCEP) and Debatepedia datasets to generate queries for summarization tasks. Then, using MMR, we ranked the sentences of the documents according to the queries. Next, we passed the ranked sentences to seven transformer-based pre-trained models to perform the summarization tasks. Finally, we used the MMR approach again to select the query relevant sentences from the generated summaries of individual pre-trained models and constructed the final summary. As indicated by the experimental results, our MMR-based approach successfully ranked and selected the most relevant sentences as summaries and showed better performance than the individual pre-trained models.

翻译：针对每个新数据集微调自然语言处理（NLP）模型需要更高的计算时间，同时伴随着碳足迹和成本的增加。然而，微调有助于预训练模型适应最新数据集；如果我们省去微调步骤，仅使用预训练模型直接生成摘要，以降低计算时间和成本，结果会如何？在本文中，我们尝试省略微调步骤，并探究基于边际最大相关性（MMR）的方法能否帮助预训练模型直接从模型预训练时未使用的新数据集中获取查询聚焦摘要。首先，我们使用维基百科时事门户（WCEP）和Debatepedia数据集进行主题建模，为摘要任务生成查询。然后，利用MMR方法，根据查询对文档中的句子进行排序。接着，我们将排序后的句子输入七个基于Transformer的预训练模型以执行摘要任务。最后，我们再次使用MMR方法从各个预训练模型生成的摘要中选取与查询相关的句子，构建最终摘要。实验结果表明，我们基于MMR的方法成功地对最相关句子进行排序和选取作为摘要，其性能优于单个预训练模型。