Automatic evaluation has greatly facilitated system development in summarization. At the same time, the use of automatic evaluation has been viewed with mistrust by many, as its accuracy and correct application are not well understood. In this paper we provide an assessment of the automatic evaluations used for multi-document summarization of news. We outline our recommendations about how any e...

In the last several years, a number of papers have addressed the area of automatic speech summarization. Many of them have applied evaluation metrics adapted from those used in speech recognition research, rather than from those used in text summarization. We consider whether ASR-inspired evaluation metrics produce different results than those taken from text summarization, and why. We evaluate...

WWW is a repository of large collection of information available in the form of unstructured documents. It is a challenging task to select the documents of interest from such a huge document pool. To fasten the process of document retrieval, text summarization technique is used. Ranking of documents is made based on the summary or the abstract provided by the authors of the document. But it is ...

Abstractive text summarization has been proposed as an alternative to the inherently limited extractive methods, but extant work is plagued with high training times. In this work, we introduce a set of extensions, including novel initialization techniques, that allow contemporary models to achieve 10x faster training time and comparable results. Our work also provides substantial evidence again...

In this paper, we propose a new method of automatic speech summarization for each utterance, where a set of words that maximizes a summarization score is extracted from automatic speech transcriptions. The summarization score indicates the appropriateness of summarized sentences. This extraction is achieved by using a dynamic programming technique according to a target summarization ratio. This...

Automatic text summarization has become important due to the rapid growth of information texts since it is very difficult for human beings to manually summarize large documents of texts. A full understanding of the document is essential to form an ideal summary. However, achieving full understanding is either difficult or impossible for computers. Therefore, selecting important sentences from t...

We present the TUT opinion summarization system which participated in the TAC 2008. The system consists of two modules: opinion/polarity automatic annotation module and fragment extraction module for summarization. Our research objective is to estimate the effectiveness of opinion/polarity annotation per sentence units for opinion summarization. The evaluation results showed that the polarity a...

Automatic summarization of reader comments in on-line news is an extremely challenging task and a capability for which there is a clear need. Work to date has focussed on producing extractive summaries using well-known techniques imported from other areas of language processing. But are extractive summaries of comments what users really want? Do they support users in performing the sorts of tas...

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...