Incorporating Multi-Hypotheses as Soft-Templates in Neural Headline Generation
Incorporating Multi-Hypotheses as Soft-Templates in Neural Headline Generation
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DOI：
10.3390/app13148478
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发表时间：
2023-07
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影响因子：
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通讯作者：
Yana A;Zhenghao Liu;Suyalatu Dong;Fanyu Bu
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文献类型：
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作者：
Yana A;Zhenghao Liu;Suyalatu Dong;Fanyu Bu
神经模型被广泛应用于标题生成。基于模板的方法是克服神经标题生成模型在生成重复或额外单词方面的缺点的一个很有前途的方向。以往的工作通常从训练数据中提取相关的标题，并将其作为软模板来指导NHG模型。然而，这些工作有两个缺点：依赖于额外的检索工具，和不确定性之间的语义一致性检索标题和源文章。NHG模型的不确定性可用于生成假设。基于经过良好训练的NHG模型生成的假设不仅包含显著信息，而且表现出多样性，使其适合作为软模板。在这项研究中，我们使用一个基本的NHG模型来生成多个不同的假设作为候选模板。然后，我们提出了一种新的基于多假设的NHG（MH-NHG）模型。对英文标题生成任务的实验表明，该系统的性能优于几个基线系统，并达到了与最先进的系统相当的性能。这表明MH-NHG可以在多个假设的指导下生成更准确的标题。
Neural models are widely applied to headline generation. Template-based methods are a promising direction to overcome the shortcomings of the neural headline generation (NHG) model in generating duplicate or extra words. Previous works often retrieve relevant headlines from the training data and adopt them as the soft template to guide the NHG model. However, these works had two drawbacks: reliance on additional retrieval tools, and uncertainty regarding semantic consistency between the retrieved headline and the source article. The NHG model uncertainty can be utilized to generate hypotheses. The hypotheses generated based on a well-trained NHG model not only contain salient information but also exhibit diversity, making them suitable as soft templates. In this study, we use a basic NHG model to generate multiple diverse hypotheses as candidate templates. Then, we propose a novel Multiple-Hypotheses-based NHG (MH-NHG) model. Experiments on English headline generation tasks demonstrate that it outperforms several baseline systems and achieves a comparable performance with the state-of-the-art system. This indicates that MH-NHG can generate more accurate headlines guided by multiple hypotheses.