In this paper we demonstrate an automatic summarization system for broadcast news shows. The proposed technique does not require ASR transcripts or human reference summaries. The system exploits the role of anchor speaker in a news show by tracking his/her speech to construct indicative extractive summaries. Speaker tracking is done by autoassociative neural network model. Summaries are generat...

Ontology summarization is a very useful technique to help users making sense of ontologies quickly. We have developed a summarization approach that linearly combines a number of criteria, drawn from cognitive science, network topology, and lexical statistics to produce ontology summaries [1]. Motivated by our later findings that the approach, in its current form, binds the criteria so tightly t...

In our second participation to the DUC evaluation, we used the SumUM system for the multidocument summarization focused by events task and summaries in response to a question task. Our multi-document summarization algorithm is based on the use of background information gathered by summarizing previous texts, which is then combined with a new document of the cluster. For producing the summaries ...

This paper discusses the convergence between question answering and multidocument summarization, pointing out implications and opportunities for knowledge transfer in both directions. As a case study in one direction, we discuss the recent development of an automatic method for evaluating definition questions based on n-gram overlap, a commonlyused technique in summarization evaluation. In the ...

The main focus of this paper is the examination of semantic modelling in the context of automatic document summarization and its evaluation. The main area of our research is extractive summarization, more specifically, contrastive opinion summarization. And as it is with all summarization tasks, the evaluation of their performance is a challenging problem on its own. Nowadays, the most commonly...

Today, with digitally stored information available in abundance, even for many minor languages, this information must by some means be filtered and extracted in order to avoid drowning in it. Automatic summarization is one such technique, where a computer summarizes a longer text to a shorter non-rendundant form. Apart from the major languages of the world there are a lot of languages for which...

We study a new content-based method for the evaluation of text summarization systems without human models which is used to produce system rankings. The research is carried out using a new content-based evaluation framework called FRESA to compute a variety of divergences among probability distributions. We apply our comparison framework to various well-established content-based evaluation measu...

In this paper we explore the benefits from and shortcomings of entity-driven noun phrase rewriting for multidocument summarization of news. The approach leads to 20% to 50% different content in the summary in comparison to an extractive summary produced using the same underlying approach, showing the promise the technique has to offer. In addition, summaries produced using entity-driven rewrite...