Automatically condensing multiple topic-related scientific papers into a
succinct and concise summary is referred to as Multi-Document Scientific
Summarization (MDSS). Currently, while commonly used abstractive MDSS methods
can generate flexible and coherent summaries, the difficulty in handling global
information and the lack of guidance during decoding still make it challenging
to generate better summaries. To alleviate these two shortcomings, this paper
introduces summary candidates into MDSS, utilizing the global information of
the document set and additional guidance from the summary candidates to guide
the decoding process. Our insights are twofold: Firstly, summary candidates can
provide instructive information from both positive and negative perspectives,
and secondly, selecting higher-quality candidates from multiple options
contributes to producing better summaries. Drawing on the insights, we propose
a summary candidates fusion framework -- Disentangling Instructive information
from Ranked candidates (DIR) for MDSS. Specifically, DIR first uses a
specialized pairwise comparison method towards multiple candidates to pick out
those of higher quality. Then DIR disentangles the instructive information of
summary candidates into positive and negative latent variables with Conditional
Variational Autoencoder. These variables are further incorporated into the
decoder to guide generation. We evaluate our approach with three different
types of Transformer-based models and three different types of candidates, and
consistently observe noticeable performance improvements according to automatic
and human evaluation. More analyses further demonstrate the effectiveness of
our model in handling global information and enhancing decoding
controllability.

本文提出了一种新的多文档科学摘要方法，利用全局信息和摘要候选项来指导解码过程，在选择更高质量的候选项和处理全局信息方面取得了明显的性能改进。

从多个排名候选项中解开指导性信息以用于多文档科学摘要

Disentangling Instructive Information from Ranked Multiple Candidates  for Multi-Document Scientific Summarization

Multi-Document Scientific Summarization (MDSS) aims to produce coherent and
concise summaries for clusters of topic-relevant scientific papers. This task
requires precise understanding of paper content and accurate modeling of
cross-paper relationships. Knowledge graphs convey compact and interpretable
structured information for documents, which makes them ideal for content
modeling and relationship modeling. In this paper, we present KGSum, an MDSS
model centred on knowledge graphs during both the encoding and decoding
process. Specifically, in the encoding process, two graph-based modules are
proposed to incorporate knowledge graph information into paper encoding, while
in the decoding process, we propose a two-stage decoder by first generating
knowledge graph information of summary in the form of descriptive sentences,
followed by generating the final summary. Empirical results show that the
proposed architecture brings substantial improvements over baselines on the
Multi-Xscience dataset.

本文提出了 KGSum 多文档科学总结的模型，重点在于在编码和解码过程中使用知识图谱来实现内容和关系建模。经验证实验结果显示，相较于基准结果，该架构带来了显著的改进。