基于transformer的词汇约束标题生成
Transformer-based Lexically Constrained Headline Generation
摘要：
本文探讨了自动标题生成方法的一种变体，其中生成的标题需要包括给定的短语，如公司或产品名称。使用基于变换器的模型的先前方法通过向编码器提供对应于给定短语的附加信息来生成包括给定短语的标题。然而，这些方法并不总是在生成的标题中包含短语。受以前基于RNN的方法从给定短语中向后和向前生成令牌序列的启发，我们提出了一种简单的基于Transformer的方法，该方法保证将给定短语包含在高质量的生成标题中。我们还考虑了一个新的标题生成策略，利用可控的生成顺序的Transformer。我们的日本新闻语料库的实验表明，我们的方法，这是保证包括短语在生成的标题，实现ROUGE分数相比，以前的基于transformer的方法。我们还表明，我们的一代策略比以前的策略表现更好。
英文摘要：
This paper explores a variant of automatic headline generation methods, where a generated headline is required to include a given phrase such as a company or a product name. Previous methods using Transformer-based models generate a headline including a given phrase by providing the encoder with additional information corresponding to the given phrase. However, these methods cannot always include the phrase in the generated headline. Inspired by previous RNN-based methods generating token sequences in backward and forward directions from the given phrase, we propose a simple Transformer-based method that guarantees to include the given phrase in the high-quality generated headline. We also consider a new headline generation strategy that takes advantage of the controllable generation order of Transformer. Our experiments with the Japanese News Corpus demonstrate that our methods, which are guaranteed to include the phrase in the generated headline, achieve ROUGE scores comparable to previous Transformer-based methods. We also show that our generation strategy performs better than previous strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
