论文标题
WSL-DS：较弱的监督学习，并在遥远的监督下进行查询的多件文档抽象摘要
WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization
论文作者
论文摘要
在以查询为中心的多文档摘要（QF-MDS）任务中，给出了一组文档和查询，目的是根据给定查询从这些文档中生成摘要。但是，这项任务的一个主要挑战是缺乏标记培训数据集的可用性。为了克服这个问题，在本文中，我们通过利用遥远的监督提出了一种新颖的弱监督学习方法。特别是，我们使用类似于目标数据集的数据集作为训练数据，在该数据中，我们利用预训练的句子相似性模型来生成来自多文件参考摘要的文档中每个单独文档的弱参考摘要。然后，我们在每个单一文档上迭代训练我们的摘要模型，以减轻在一次训练多个文档（即长序列）中训练神经摘要模型时发生的计算复杂性问题。文档理解会议（DUC）数据集的实验结果表明，我们提出的方法在各种评估指标方面为新的最新结果设定了新的最新结果。
In the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents based on the given query. However, one major challenge for this task is the lack of availability of labeled training datasets. To overcome this issue, in this paper, we propose a novel weakly supervised learning approach via utilizing distant supervision. In particular, we use datasets similar to the target dataset as the training data where we leverage pre-trained sentence similarity models to generate the weak reference summary of each individual document in a document set from the multi-document gold reference summaries. Then, we iteratively train our summarization model on each single-document to alleviate the computational complexity issue that occurs while training neural summarization models in multiple documents (i.e., long sequences) at once. Experimental results in Document Understanding Conferences (DUC) datasets show that our proposed approach sets a new state-of-the-art result in terms of various evaluation metrics.
