Text summarization is the process of automatically creating a compressed version of a given document preserving its information content. There are two types of summarization: extractive and abstractive. Extractive summarization methods simplify the problem of summarization into the problem of selecting a representative subset of the sentences in the original documents. Abstractive summarization...

Multi-document summarization (MDS) aims to generate a summary for number of related documents. We propose HGSum — an MDS model that extends encoder-decoder architecture incorporate heterogeneous graph represent different semantic units (e.g., words and sentences) the This contrasts with existing models which do not consider edge types graphs as such capture diversity relationships in To preserv...

The ability to convey relevant and diverse information is critical in multi-document summarization yet remains elusive for neural seq-to-seq models whose outputs are often redundant fail correctly cover important details. In this work, we propose an attention mechanism which encourages greater focus on relevance diversity. Attention weights computed based (proportional) probabilities given by D...

Document summarization is a technique aimed to automatically extract main ideas from electronic documents. In this paper, we propose a novel algorithm, called TriangleSum for key sentence extraction from single document based on graph theory. The algorithm builds a dependency graph for the underlying document based on co-occurrence relation as well as syntactic dependency relations. The nodes r...

With vast amounts of text being available in electronic format, such as news and social media, automatic multi-document summarization can help extract the most important information. We present and evaluate a novel method for automatic extractive multi-document summarization. The method is purely combinatorial, based on bicliques in the bipartite word-sentence occurrence graph. It is particular...