Non-Parametric Memory Guidance for Multi-Document Summarization
Florian Baud Alex Aussem
Multi-document summarization (MDS) is a difficult task in Natural Language
Processing, aiming to summarize information from several documents. However,
the source documents are often insufficient to obtain a qualitative summary. We
propose a retriever-guided model combined with non-parametric memory for
summary generation. This model retrieves relevant candidates from a database
and then generates the summary considering the candidates with a copy mechanism
and the source documents. The retriever is implemented with Approximate Nearest
Neighbor Search (ANN) to search large databases. Our method is evaluated on the
MultiXScience dataset which includes scientific articles. Finally, we discuss
our results and possible directions for future work.