Enhancing Multi-Document Summarization with Cross-Document Graph-based Information Extraction (2023)

Abstract: Information extraction (IE) and summarization are closely related, both tasked with presenting a subset of the information contained in a natural language text. However, while IE extracts structural representations, summarization aims to abstract the most salient information into a generated text summary – thus potentially encountering the technical limitations of current text generation methods (e.g., hallucination). To mitigate this risk, this work uses structured IE graphs to enhance the abstractive summarization task. Specifically, we focus on improving Multi-Document Summarization (MDS) performance by using cross-document IE output, incorporating two novel components: (1) the use of auxiliary entity and event recognition systems to focus the summary generation model; (2) incorporating an alignment loss between IE nodes and their text spans to reduce inconsistencies between the IE graphs and text representations. Operationally, both the IE nodes and corresponding text spans are projected into the same embedding space and pairwise distance is minimized. Experimental results on multiple MDS benchmarks show that summaries generated by our model are more factually consistent with the source documents than baseline models while maintaining the same level of abstractiveness.

## problem formulation our problem definition follows the typical formulation of abstractive multi-document summarization (mds). specifically, given a cluster of input documents @xmath0, d 2 , • • • , d n }, we aim to build a model to generate a summary s of the document cluster. in this paper, we particularly focus on using ie to enhance summarization using the ie graph g merged from the individual graphs ## approach in this paper, our main goal is to improve multidocument summarization (mds) with the extracted cross-document ie graph. as illustrated in figure 2 , we first concatenate all documents in a cluster and feed this concatenated input into a longformer encoder @xcite ) that is capable of handling long text sequences. we also use the crossdocument ie system to obtain a cluster-level ie graph, as shown in the example highlighted in figure 1 , and use a graph attention network to obtain the node representations. during training, in addition to the cross-entropy summary loss between the generated and the ground-truth summaries, we propose two additional novel training objectives: (1) an entity and event recognition task that makes the model aware of the locations of important events and entities; and (2) an alignment loss between the ie graph nodes and their corresponding text spans to ensure that they are factually consistent in the latent space. we will go into details of our model design in the following sections.