This paper reports the latest development of The Halliday Centre Tagger (the Tagger), an online platform provided with semi-automatic features to facilitate text annotation and analysis. The Tagger features a web-based architecture with all functionalities and file storage space provided online, and a theory-neutral design where users can define their own labels for annotating various kinds of ...

Automatic Text Summarization has received a great deal of attention in the past couple of decades. It has gained a lot of interest especially with the proliferation of the Internet and the new technologies. Arabic as a language still lacks research in the field of Information Retrieval. In this paper, we explore lexical cohesion using lexical chains for an extractive summarization system for Ar...

The discovery of semantic relations from text becomes increasingly important for applications such as Question Answering, Information Extraction, Text Summarization, Text Understanding, and others. The semantic relations are detected by checking selectional constraints. This paper presents a method and its results for learning semantic constraints to detect part-whole relations. Twenty constrai...

Recently, the credibility of information on the Web has become an important issue. In addition to telling about content of source documents, indicating how to interpret the content, especially showing interpretation of the relation between statements appeared to contradict each other, is important for helping a user judge the credibility of information. In this paper, we will describe the purpo...

We present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of documentsummary pairs. When used for singledocument summarization, our approach, combined with the “beginning of document” heuristic, outperforms a state-ofthe-art summarizer and the beginning-ofarticle baseline in both automatic and manual evaluations. These results represent an impo...

The tweet contextualization INEX task at CLEF 2012 consists of the developing of a system that, given a tweet, can provide some context about the subject of the tweet, in order to help the reader to understand it. This context should take the form of a readable summary, not exceeding 500 words, composed of passages from a provided Wikipedia corpus. Our general approach to get this objective is ...

We report on a language resource consisting of 2000 annotated bibliography entries, which is being analyzed as part of our research on indicative document summarization. We show how annotated bibliographies cover certain aspects of summarization that have not been well-covered by other summary corpora, and motivate why they constitute an important form to study for information retrieval. We det...

We suggest a new method for the task of extractive text summarization using graph-based ranking algorithms. The main idea of this paper is to rank Maximal Frequent Sequences (MFS) in order to identify the most important information in a text. MFS are considered as nodes of a graph in term selection step, and then are ranked in term weighting step using a graphbased algorithm. We show that the p...

We discuss those techniques which, in the opinion of the authors, are needed to support robust automatic summarization. Many of these methods are already incorporated in a multi-lingual summarization system, MINDS, developed at CRL. The approach is sentence selection, but includes techniques to improve coherence and also to perform sentence reduction. Our methods are in distinct contrast to tho...

A system allowing extractive automatic summarization of textual documents is presented. The system is based on the cohesive properties of text, namely lexical chains, co-reference chains and named entity chains. In this way the system extend the well known lexicalchaining paradigm for summarization. The system has been applied to summarization tasks on Spanish agency news. Results of its evalua...