We participated in three multi-document summarization tasks at the DUC-2003 formal run and evaluated the performance of our summarization system. Our summarization system based on sentence extraction also incorporated a module to estimate similarity between sentences for multi-document summarization. The similarity information was used for selecting the representative sentence among similar sen...

A New Approach to Automatic Summarization by Using Latent Dirichlet Allocation in Conditional Random Field Xiaofeng Wu, Chengqing Zong (National Lab of Pattern Recognition, Institute of Automation, CAS, Beijing 100190, China) Abustract: In recent years, Latent Dirichlet Allocation(LDA) has been used more and more in Document Clustering, Classification, Segmentation, and some one has used it in ...

Multi-document summarization is a fundamental tool for understanding documents. Given a collection of documents, most of existing multidocument summarization methods automatically generate a static summary for all the users using unsupervised learning techniques such as sentence ranking and clustering. However, these methods almost exclude human from the summarization process. They do not allow...

Although single-document summarization is a well-studied task, the nature of multidocument summarization is only beginning to be studied in detail. While close attention has been paid to what technologies are necessary when moving from single to multi-document summarization, the properties of humanwritten multi-document summaries have not been quantified. In this paper, we empirically character...

Summarization, an extremely important technique in Data Mining is an automatic learning technique aimed to extract the most valuable information from a large size document or the articles. The goal is to create the summary of the document, but substantially different from each other. Text Document summarization refers to the summarization of text documents based upon their content. The proposed...

Headline generation is a task of abstractive text summarization, and previously suffers from the immaturity of natural language generation techniques. Recent success of neural sentence summarization models shows the capacity of generating informative, fluent headlines conditioned on selected recapitulative sentences. In this paper, we investigate the extension of sentence summarization models t...

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...