DUC 2004

Multidocument extractive summarization relies on the concept of sentence centrality to identify the most important sentences in a document. Central- ity is typically defined in terms of the presence of …...

In this paper we describe a biography summarization system using sentence classification and ideas from information retrieval. Although the individual techniques are not new, assembling and applying them to generate …...

We design a class of submodular functions meant for document summarization tasks. These functions each combine two terms, one which encourages the summary to be representative of the corpus, and …...

We present a new approach to the problem of automatic text summarization called Au- tomatic Summarization using Reinforcement Learning (ASRL) in this paper, which models the process of constructing a …...

Document summarization is an important task in the area of natural language processing, which aims to extract the most important in- formation from a single document or a clus- ter …...

We propose a new optimization frame- work for summarization by generalizing the submodular framework of (Lin and Bilmes, 2011). In our framework the sum- marization desideratum is expressed as a …...

AbstractIn the period since 2004, many novel sophisticated approaches for generic multi-document summarization have been developed. Intuitive simple approaches have also been shown to perform unexpectedly well for the task. …...

This paper explores alternate algorithms, reward functions and feature sets for per- forming multi-document summarization using reinforcement learning with a high focus on reproducibility. We show that ROUGE results can …...

AbstractMulti-document summarization provides users with a short text that summarizes the information in a set of related documents. This paper introduces affinity-preserving random walk to the summarization task, which preserves …...

AbstractWe propose a neural multi-document summarization system that incorporates sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural …...

AbstractWe propose a selective encoding model to extend the sequence-to-sequence framework for abstractive sentence summarization. It consists of a sentence encoder, a selective gate network, and an attention equipped decoder. …...

AbstractMost work on multi-document summarization has focused on generic summarization of information present in each individual document set. However, the under-explored setting of update summarization, where the goal is to …...

AbstractText clustering methods were traditionally incorporated into multi-document summarization (MDS) as a means for coping with considerable information repetition. Particularly, clusters were leveraged to indicate information saliency as well as …...