NoticIA: A Clickbait Article Summarization Dataset in Spanish
This addresses the need for better text summarization in Spanish for clickbait articles, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of summarizing Spanish clickbait news articles by creating NoticIA, a dataset of 850 articles with human-written summaries, and used it to train ClickbaitFighter, a model that achieves near-human performance.
We present NoticIA, a dataset consisting of 850 Spanish news articles featuring prominent clickbait headlines, each paired with high-quality, single-sentence generative summarizations written by humans. This task demands advanced text understanding and summarization abilities, challenging the models' capacity to infer and connect diverse pieces of information to meet the user's informational needs generated by the clickbait headline. We evaluate the Spanish text comprehension capabilities of a wide range of state-of-the-art large language models. Additionally, we use the dataset to train ClickbaitFighter, a task-specific model that achieves near-human performance in this task.