Although domain shift has been well explored in many NLP applications, it
still has received little attention in the domain of extractive text
summarization. As a result, the model is under-utilizing the nature of the
training data due to ignoring the difference in the distribution of training
sets and shows poor generalization on the unseen domain.
With the above limitation in mind, in this paper, we first extend the
conventional definition of the domain from categories into data sources for the
text summarization task. Then we re-purpose a multi-domain summarization
dataset and verify how the gap between different domains influences the
performance of neural summarization models.
Furthermore, we investigate four learning strategies and examine their
abilities to deal with the domain shift problem.
Experimental results on three different settings show their different
characteristics in our new testbed.
Our source code including \textit{BERT-based}, \textit{meta-learning} methods
for multi-domain summarization learning and the re-purposed dataset
\textsc{Multi-SUM} will be available on our project:
http://pfliu.com/TransferSum/.

本文研究了文本摘要技术中的领域转移问题，提出了将领域的定义从类别扩展到数据源，并探讨了四种不同的学习策略以解决领域转移问题，在新测试中呈现出不同的性能特点。

探索抽取式文本摘要中的领域转移

Exploring Domain Shift in Extractive Text Summarization

The success of neural summarization models stems from the meticulous
encodings of source articles. To overcome the impediments of limited and
sometimes noisy training data, one promising direction is to make better use of
the available training data by applying filters during summarization. In this
paper, we propose a novel Bi-directional Selective Encoding with Template
(BiSET) model, which leverages template discovered from training data to softly
select key information from each source article to guide its summarization
process. Extensive experiments on a standard summarization dataset were
conducted and the results show that the template-equipped BiSET model manages
to improve the summarization performance significantly with a new state of the
art.

提出了一种名为 BiSET 模型的新型双向选择性编码模型，该模型利用从训练数据中发现的模板，软性选择每个源文章中的关键信息，指导其压缩过程，实验结果显示，该模型显著提高了压缩性能，取得了新的最高水平。