标题： 从预训练语言模型进行迁移学习可提高端到端语音摘要生成 显示英文标题

标题： Transfer Learning from Pre-trained Language Models Improves End-to-End Speech Summarization

摘要： 端到端语音摘要（E2E SSum）直接将输入语音总结为易于阅读的短句，仅使用一个模型。 这种方法很有前景，因为它与传统的级联方法相反，可以利用完整的声学信息并减轻转录错误的传播。 然而，由于收集语音-摘要对的成本很高，E2E SSum模型往往面临训练数据不足和输出不自然句子的问题。 为克服这一缺点，我们首次提出通过迁移学习将一个预训练语言模型（LM），该模型在生成自然句子方面具有很强的能力，集成到E2E SSum解码器中。 此外，为了缩小独立预训练编码器和解码器之间的差距，我们还提出转移基线E2E SSum编码器，而不是通常使用的自动语音识别编码器。 实验结果表明，所提出的模型优于基线模型和数据增强模型。 显示英文摘要

摘要： End-to-end speech summarization (E2E SSum) directly summarizes input speech into easy-to-read short sentences with a single model. This approach is promising because it, in contrast to the conventional cascade approach, can utilize full acoustical information and mitigate to the propagation of transcription errors. However, due to the high cost of collecting speech-summary pairs, an E2E SSum model tends to suffer from training data scarcity and output unnatural sentences. To overcome this drawback, we propose for the first time to integrate a pre-trained language model (LM), which is highly capable of generating natural sentences, into the E2E SSum decoder via transfer learning. In addition, to reduce the gap between the independently pre-trained encoder and decoder, we also propose to transfer the baseline E2E SSum encoder instead of the commonly used automatic speech recognition encoder. Experimental results show that the proposed model outperforms baseline and data augmented models.