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Leveraging Graph to Improve Abstractive Multi-Document Summarization
TLDR
A neural abstractive multi-document summarization (MDS) model which can leverage well-known graph representations of documents, to more effectively process multiple input documents and produce abstractive summaries is developed.Abstract:
Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries. In this paper, we develop a neural abstractive multi-document summarization (MDS) model which can leverage well-known graph representations of documents such as similarity graph and discourse graph, to more effectively process multiple input documents and produce abstractive summaries. Our model utilizes graphs to encode documents in order to capture cross-document relations, which is crucial to summarizing long documents. Our model can also take advantage of graphs to guide the summary generation process, which is beneficial for generating coherent and concise summaries. Furthermore, pre-trained language models can be easily combined with our model, which further improve the summarization performance significantly. Empirical results on the WikiSum and MultiNews dataset show that the proposed architecture brings substantial improvements over several strong baselines.read more
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A Survey of Knowledge-Enhanced Text Generation.
TL;DR: A comprehensive review of the research on knowledge-enhanced text generation over the past five years is presented, which includes two parts: (i) general methods and architectures for integrating knowledge into text generation; (ii) specific techniques and applications according to different forms of knowledge data.
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Multi-document Summarization via Deep Learning Techniques: A Survey.
TL;DR: This survey, the first of its kind, systematically overviews the recent deep learning based MDS models and proposes a novel taxonomy to summarize the design strategies of neural networks and conduct a comprehensive summary of the state of the art.
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MS2: Multi-Document Summarization of Medical Studies.
TL;DR: This work releases MSˆ2 (Multi-Document Summarization of Medical Studies), a dataset of over 470k documents and 20K summaries derived from the scientific literature that facilitates the development of systems that can assess and aggregate contradictory evidence across multiple studies, and is the first large-scale, publicly available multi-document summarization dataset in the biomedical domain.
Proceedings ArticleDOI
Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters
TL;DR: This paper presents an efficient graph-enhanced approach to multi-document summarization (MDS) with an encoder-decoder Transformer model that leads to significant improvements on the Multi-News dataset, overall leading to an average 1.8 ROUGE score improvement over previous work.
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FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations
TL;DR: FactGraph is proposed, a method that decomposes the document and the summary into structured meaning representations (MR), which are more suitable for factuality evaluation and improves performance on identifying content verifiability errors and better captures subsentence-level factual inconsistencies.
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