Headline-Guided Extractive Summarization for Thai News Articles
Text summarization is a process of condensing lengthy texts while preserving their essential information. Previous studies have predominantly focused on high-resource languages, while low-resource languages like Thai have received less attention. Furthermore, earlier extractive summarization models...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10870350/ |
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| author | Pimpitchaya Kositcharoensuk Nakarin Sritrakool Ploy N. Pratanwanich |
| author_facet | Pimpitchaya Kositcharoensuk Nakarin Sritrakool Ploy N. Pratanwanich |
| author_sort | Pimpitchaya Kositcharoensuk |
| collection | DOAJ |
| description | Text summarization is a process of condensing lengthy texts while preserving their essential information. Previous studies have predominantly focused on high-resource languages, while low-resource languages like Thai have received less attention. Furthermore, earlier extractive summarization models for Thai texts have primarily relied on the article’s body, without considering the headline. This omission can result in the exclusion of key sentences from the summary. To address these limitations, we propose CHIMA, an extractive summarization model that incorporates the contextual information of the headline for Thai news articles. Our model utilizes a pre-trained language model to capture complex language semantics and assigns a probability to each sentence to be included in the summary. By leveraging the headline to guide sentence selection, CHIMA enhances the model’s ability to recover important sentences and discount irrelevant ones. Additionally, we introduce two strategies for aggregating headline-body similarities, simple average and harmonic mean, providing flexibility in sentence selection to accommodate varying writing styles. Experiments on publicly available Thai news datasets demonstrate that CHIMA outperforms baseline models across ROUGE, BLEU, and F1 scores. These results highlight the effectiveness of incorporating the headline-body similarities as model guidance. The results also indicate an enhancement in the model’s ability to recall critical sentences, even those scattered throughout the middle or end of the article. With this potential, headline-guided extractive summarization offers a promising approach to improve the quality and relevance of summaries for Thai news articles. |
| format | Article |
| id | doaj-art-2b8d2bd4a0a64cc8808ac49fcb00e247 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2b8d2bd4a0a64cc8808ac49fcb00e2472025-02-11T00:01:05ZengIEEEIEEE Access2169-35362025-01-0113243682438210.1109/ACCESS.2025.353832910870350Headline-Guided Extractive Summarization for Thai News ArticlesPimpitchaya Kositcharoensuk0https://orcid.org/0009-0002-7166-1001Nakarin Sritrakool1Ploy N. Pratanwanich2https://orcid.org/0000-0003-3684-6967Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, ThailandNational Institute of Informatics, Tokyo, JapanDepartment of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok, ThailandText summarization is a process of condensing lengthy texts while preserving their essential information. Previous studies have predominantly focused on high-resource languages, while low-resource languages like Thai have received less attention. Furthermore, earlier extractive summarization models for Thai texts have primarily relied on the article’s body, without considering the headline. This omission can result in the exclusion of key sentences from the summary. To address these limitations, we propose CHIMA, an extractive summarization model that incorporates the contextual information of the headline for Thai news articles. Our model utilizes a pre-trained language model to capture complex language semantics and assigns a probability to each sentence to be included in the summary. By leveraging the headline to guide sentence selection, CHIMA enhances the model’s ability to recover important sentences and discount irrelevant ones. Additionally, we introduce two strategies for aggregating headline-body similarities, simple average and harmonic mean, providing flexibility in sentence selection to accommodate varying writing styles. Experiments on publicly available Thai news datasets demonstrate that CHIMA outperforms baseline models across ROUGE, BLEU, and F1 scores. These results highlight the effectiveness of incorporating the headline-body similarities as model guidance. The results also indicate an enhancement in the model’s ability to recall critical sentences, even those scattered throughout the middle or end of the article. With this potential, headline-guided extractive summarization offers a promising approach to improve the quality and relevance of summaries for Thai news articles.https://ieeexplore.ieee.org/document/10870350/Document analysisextractive text summarizationinformation retrievalnatural language processingnatural language understandingpattern recognition |
| spellingShingle | Pimpitchaya Kositcharoensuk Nakarin Sritrakool Ploy N. Pratanwanich Headline-Guided Extractive Summarization for Thai News Articles IEEE Access Document analysis extractive text summarization information retrieval natural language processing natural language understanding pattern recognition |
| title | Headline-Guided Extractive Summarization for Thai News Articles |
| title_full | Headline-Guided Extractive Summarization for Thai News Articles |
| title_fullStr | Headline-Guided Extractive Summarization for Thai News Articles |
| title_full_unstemmed | Headline-Guided Extractive Summarization for Thai News Articles |
| title_short | Headline-Guided Extractive Summarization for Thai News Articles |
| title_sort | headline guided extractive summarization for thai news articles |
| topic | Document analysis extractive text summarization information retrieval natural language processing natural language understanding pattern recognition |
| url | https://ieeexplore.ieee.org/document/10870350/ |
| work_keys_str_mv | AT pimpitchayakositcharoensuk headlineguidedextractivesummarizationforthainewsarticles AT nakarinsritrakool headlineguidedextractivesummarizationforthainewsarticles AT ploynpratanwanich headlineguidedextractivesummarizationforthainewsarticles |