This paper presents our work on the evaluation of syntactic based sentence compression for automatic text summarization. Sentence compression techniques can contribute to text summarization by removing redundant and irrelevant information and allowing more space for more relevant content. However, very little work has focused on evaluating the contribution of this idea for summarization. In thi...

This paper 1 develops a method for combining queryrelevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in reranking retrieved documents and in selecting appropriate passages for text summarization. Preliminary results indicate some benefits for MMR dive...

Most of the text summarization research carried out to date has been concerned with the summarization of short documents (e.g., news stories, technical reports), and very little work if any has been done on the summarization of very long documents. In this paper, we try to address this gap and explore the problem of book summarization. We introduce a new data set specifically designed for the e...

Automatic text summarization, the reduction of a text to its essential content is fundamental for an on-line information society. Although many summarization algorithms exist, there are few tools or infrastructures providing capabilities for developing summarization applications. This paper presents a new version of SUMMA, a text summarization toolkit for the development of adaptive summarizati...

In this paper, I present initial study of new term vector generation methods. The Random Manhattan Indexing and the Skip-gram model were introduced as novel techniques of term vector generation with interesting features. The purpose of this study is to determine whether the methods are suitable for the Summec: A Summarization Engine for Czech. The Summec already use Heuristic, TF-IDF and Latent...

We discuss several improvements to the Question Generation Shared Task Evaluation Challenge (QGSTEC) system developed at the University of Pennsylvania in 2010. In addition to enhancing the question generation rules, we have implemented two new components to improve the ranking process. We use topic scoring, a technique developed for summarization, to identify important information for question...

This paper introduces a novel hierarchical summarization approach for automatic multidocument summarization. By creating a hierarchical representation of the words in the input document set, the proposed approach is able to incorporate various objectives of multidocument summarization through an integrated framework. The evaluation is conducted on the DUC 2007 data set.

Title of dissertation: STATISTICAL ANALYSIS OF AUTOMATIC SUMMARIZATION EVALUATION Peter A. Rankel, Doctor of Philosophy, 2016 Dissertation directed by: Professor Eric V. Slud Department of Mathematics Dr. John M. Conroy IDA/Center for Computing Sciences This dissertation applies statistical methods to the evaluation of automatic summarization using data from the Text Analysis Conferences in 200...

This is the first time we participate in TAC. In this report, we present our extractive summarization system on both initial and update summarization tracks of TAC 2010. We introduce an integrated method to generate all summaries. The TAC evaluation of results show that our summarization method is feasible but it has to be improved in future.