CHIMA: 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 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.

LOTR: Face Landmark Localization Using Localization Transformer

This paper presents a novel Transformer-based facial landmark localization network named Localization Transformer (LOTR). The proposed framework is a direct coordinate regression approach leveraging a Transformer network to better utilize the spatial information in the feature map. An LOTR model consists of three main modules: 1) a visual backbone that converts an input image into a feature map, 2) a Transformer module that improves the feature representation from the visual backbone, and 3) a landmark prediction head that directly predicts the landmark coordinates from the Transformer's representation. Given cropped-and-aligned face images, the proposed LOTR can be trained end-to-end without requiring any post-processing steps. This paper also introduces the smooth-Wing loss function, which addresses the gradient discontinuity of the Wing loss, leading to better convergence than standard loss functions such as L1, L2, and Wing loss. Experimental results on the JD landmark dataset provided by the First Grand Challenge of 106-Point Facial Landmark Localization indicate the superiority of LOTR over the existing methods on the leaderboard and two recent heatmap-based approaches.