Improving truthfulness of headline generation
Witryna1 kwi 2024 · The motivation of this research is to explore the essence of the auto-summarization generation by simulating people’s thinking logic in the process of writing abstracts, giving deep learning a certain degree of interpretability and reasoning. 2. There are a lot of repetition problems in the abstracts generated in the previous related studies. WitrynaThis paper explores improving the truthfulness in headline generation on two popular datasets. Analyzing headlines generated by the state-of-the-art encoder-decoder model, we show that the model sometimes generates untruthful headlines. We conjecture that one of the reasons lies in untruthful supervision data used for training the model.
Improving truthfulness of headline generation
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WitrynaACL2024: Improving Truthfulness of Headline Generation Improving Truthfulness of Headline Generation Kazuki Matsumaru , Sho Takase , Naoaki Okazaki Abstract … WitrynaThis paper explores improving the truthfulness in headline generation on two popular datasets. Analyzing headlines generated by the state-of-the-art encoder-decoder …
Witryna2 maj 2024 · This paper explores improving the truthfulness in headline generation on two popular datasets. Analyzing headlines generated by the state-of-the-art encoder … Witryna2 maj 2024 · Thispaper explores improving the truthfulness inheadline generation on two popular datasets.Analyzing headlines generated by the state-of-the-art encoder …
WitrynaThis paper explores improving the truthfulness in headline generation on two popular datasets. Analyzing headlines generated by the state-of-the-art encoder-decoder … Witrynaheadline-entailment Datasets created in the paper Improving Truthfulness of Headline Generation. Gigaword Entailment Dataset We put datasets of annotation results we …
WitrynaThis paper explores improving the truthfulness in headline generation on two popular datasets. Analyzing headlines generated by the state-of-the-art encoder-decoder model, we show that the model sometimes generates untruthful headlines. We conjecture that one of the reasons lies in untruthful supervision data used for training the model.
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