Multi-document summarization (MDS) is a challenging task, often decomposed to
subtasks of salience and redundancy detection, followed by text generation. In
this context, alignment of corresponding sentences between a reference summary
and its source documents has been leveraged to generate training data for some
of the component tasks. Yet, this enabling alignment step has usually been
applied heuristically on the sentence level on a limited number of subtasks. In
this paper, we propose extending the summary-source alignment framework by (1)
applying it at the more fine-grained proposition span level, (2) annotating
alignment manually in a multi-document setup, and (3) revealing the great
potential of summary-source alignments to yield several datasets for at least
six different tasks. Specifically, for each of the tasks, we release a manually
annotated test set that was derived automatically from the alignment
annotation. We also release development and train sets in the same way, but
from automatically derived alignments. Using the datasets, each task is
demonstrated with baseline models and corresponding evaluation metrics to spur
future research on this broad challenge.

扩展摘要 - 源数据对齐框架，应用于提取多个任务的数据集，包括文档摘要、内容突出点检测、冗余检测等。

摘要 - 来源对齐的力量

The Power of Summary-Source Alignments

In this paper, we present a novel framework for data redundancy measurement
based on probabilistic modeling of datasets, and a new criterion for redundancy
detection that is resilient to noise. We also develop new methods for data
redundancy reduction using both deterministic and stochastic optimization
techniques. Our framework is flexible and can handle different types of
features, and our experiments on benchmark datasets demonstrate the
effectiveness of our methods. We provide a new perspective on feature
selection, and propose effective and robust approaches for both supervised and
unsupervised learning problems.

基于数据集的概率建模和对噪声的鲁棒性广泛测量，本文提出了一种新的数据冗余度测量框架和检测标准，并使用确定性和随机优化技术开发了新的数据冗余度减少方法。我们在基准数据集上进行的实验表明了我们方法的有效性，并为监督和无监督学习问题提出了一种新的特征选择视角和提出了有效且鲁棒的方法。