The centroid method is a simple approach for extractive multi-document
summarization and many improvements to its pipeline have been proposed. We
further refine it by adding a beam search process to the sentence selection and
also a centroid estimation attention model that leads to improved results. We
demonstrate this in several multi-document summarization datasets, including in
a multilingual scenario.

通过在句子选择中添加一种束搜索过程和一个质心估计关注模型，我们进一步改进了质心方法，在多个多文档摘要数据集中展示出了改进的结果，包括多语言情景的应用。

监督中心点基准用于提取式多文档摘要

Supervising the Centroid Baseline for Extractive Multi-Document  Summarization

Most of existing extractive multi-document summarization (MDS) methods score
each sentence individually and extract salient sentences one by one to compose
a summary, which have two main drawbacks: (1) neglecting both the intra and
cross-document relations between sentences; (2) neglecting the coherence and
conciseness of the whole summary. In this paper, we propose a novel MDS
framework (SgSum) to formulate the MDS task as a sub-graph selection problem,
in which source documents are regarded as a relation graph of sentences (e.g.,
similarity graph or discourse graph) and the candidate summaries are its
sub-graphs. Instead of selecting salient sentences, SgSum selects a salient
sub-graph from the relation graph as the summary. Comparing with traditional
methods, our method has two main advantages: (1) the relations between
sentences are captured by modeling both the graph structure of the whole
document set and the candidate sub-graphs; (2) directly outputs an integrate
summary in the form of sub-graph which is more informative and coherent.
Extensive experiments on MultiNews and DUC datasets show that our proposed
method brings substantial improvements over several strong baselines. Human
evaluation results also demonstrate that our model can produce significantly
more coherent and informative summaries compared with traditional MDS methods.
Moreover, the proposed architecture has strong transfer ability from single to
multi-document input, which can reduce the resource bottleneck in MDS tasks.
Our code and results are available at:
https://github.com/PaddlePaddle/Research/tree/master/NLP/EMNLP2021-SgSum.

本文提出了一种名为 SgSum 的新型 EMDS 框架，将 MDS 任务作为一个子图选择问题，通过构建关系图来选择一个显著的子图，而不是单独评分和提取句子，从而解决了现有方法中忽略句子关系和整个摘要连贯性和凝练性的问题。与传统方法相比，SgSum 模型能够由于模型对句子关系的建模和直接输出更加丰富和连贯的子图形式摘要而获得显著优势，且在 MultiNews 和 DUC 数据集上进行的实验表明，我们的方法比多个强基线模型都有显著改进。

SgSum: 将多文档摘要转换为子图选择

SgSum: Transforming Multi-document Summarization into Sub-graph  Selection

We study the problem of predicting a set or list of options under knapsack
constraint. The quality of such lists are evaluated by a submodular reward
function that measures both quality and diversity. Similar to DAgger (Ross et
al., 2010), by a reduction to online learning, we show how to adapt two
sequence prediction models to imitate greedy maximization under knapsack
constraint problems: CONSEQOPT (Dey et al., 2012) and SCP (Ross et al., 2013).
Experiments on extractive multi-document summarization show that our approach
outperforms existing state-of-the-art methods.

本文研究了在背包约束下预测选项集或列表的问题，并通过在线学习的降级模型，证明了如何使两个序列预测模型适应贪心的最大化背包约束问题，实验结果表明，我们的方法优于现有的最先进方法。