  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.
