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...

Scalable semantic data processing has become a crucial issue for practical applications of the Semantic Web. In this paper, we propose an approach of scalable semantic data processing by knowledge summarization. The main idea is to express scalable semantic data on different abstraction and summarization levels to reduce their cardinalities, so that they can be processed efficiently. The notion...

We study correlation of rankings of text summarization systems using evaluation methods with and without human models. We apply our comparison framework to various well-established contentbased evaluation measures in text summarization such as coverage, Responsiveness, Pyramids and ROUGE studying their associations in various text summarization tasks including generic and focus-based multi-docu...

Recent years have shown that graphs are an adequate text representation model for summarization. For this years’ TAC update summarization challenge, we extended our graph-based summarization system with coreference relations and sentence compression. Our results show that using coreference relations did not result in a significant performance gain; sentence compression had a negative effect on ...

ive summarization is one of the main goals of text summarization research, but also one of its greatest challenges. The authors of a recent literature review (Lloret and Palomar 2012) even conclude that “abstractive paradigms [...] will become one of the main challenges to solve” in text summarization. In building an abstractive summarization system, however, it is often hard to imagine where t...

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...

Due to the rapid-developed Internet and with the big data era coming, the automatic summarization research has been emerged a popular research topic. The aim of automatic summarization is in attempt to select important text or spoken sentence to represent the topic (theme) of original text or spoken document according to a predefined summarization ratio. In this study we frame automatic summari...

META: Multi-resolution Framework for Event Summarization Report Title Event summarization is an effective process that mines and organizes event patterns to represent the original events. It allows the analysts to quickly gain the general idea of the events. In recent years, several event summarization algorithms have been proposed, but they all focus on how to find out the optimal summarizatio...