Fast Hybrid Approach for Thai News Summarization

News summarization presents a significant challenge in Natural Language Processing (NLP). Lengthy news articles not only consume valuable time but also lead to confusion regarding key points. The ideal news summarization should swiftly produce a succinct summary while retaining the essence of the information conveyed by the news writer. While intelligent chatbots like ChatGPT and Gemini offer user-friendly text summarization, their embedded Large Language Model (LLM) cannot be downloaded for private use. Moreover, implementing them in a business process can be expensive, both in terms of pay-per-use costs and response time. The objective of this research is to develop a private Thai news summarization that effectively extracts sentences encapsulating the main idea and abstractly summarizes them. The proposed model consists of two components. The first extracts a contiguous region containing important sentences using the TextRank algorithm, while the second employs the finetuned mBART as an LLM to generate the abstractive summary from the previously extracted sentences. In other words, the proposed model extracts an important news region before passing it to mBART. This approach produces a news summary with key information and a syntactic style akin to the natural Thai language. We evaluate the summarization quality by ROUGE scores and BERTScore (precision, recall, and F1-score). The evaluation metrics Experimental results on the ThaiSum dataset show relatively high ROUGE scores and BERTScore for the proposed model compared to most of the other approaches. Furthermore, it significantly reduces the runtime, keeping it within a reasonable limit.