论文标题
基于启发式的训练以改善几杆多镜头对话框摘要
Heuristic-based Inter-training to Improve Few-shot Multi-perspective Dialog Summarization
论文作者
论文摘要
许多组织要求其客户护理代理商手动总结与客户的对话。这些摘要对于组织的决策目的至关重要。需要创建的摘要的观点取决于摘要的应用。通过这项工作，我们研究了支持代理商和客户之间客户护理对话的多人汇总。我们观察到，与不同观点的摘要相关的启发式方法不同，并探索这些启发式方法，以创建弱标记的数据，用于对模型进行中间培训，然后以稀缺的人类注释的摘要进行微调。最重要的是，我们表明我们的方法支持模型，以生成具有少量注释数据的多观点摘要。例如，我们的方法仅通过仅使用7 \％的原始数据训练，实现了经过原始数据训练的模型的94％\％的性能（Rouge-2）。
Many organizations require their customer-care agents to manually summarize their conversations with customers. These summaries are vital for decision making purposes of the organizations. The perspective of the summary that is required to be created depends on the application of the summaries. With this work, we study the multi-perspective summarization of customer-care conversations between support agents and customers. We observe that there are different heuristics that are associated with summaries of different perspectives, and explore these heuristics to create weak-labeled data for intermediate training of the models before fine-tuning with scarce human annotated summaries. Most importantly, we show that our approach supports models to generate multi-perspective summaries with a very small amount of annotated data. For example, our approach achieves 94\% of the performance (Rouge-2) of a model trained with the original data, by training only with 7\% of the original data.
