We address the problem of sentence alignment for monolingual corpora, a phenomenon distinct from alignment in parallel corpora. Aligning large comparable corpora automatically would provide a valuable resource for learning of text-totext rewriting rules. We incorporate context into the search for an optimal alignment in two complementary ways: learning rules for matching paragraphs using topic structure and further refining the matching through local alignment to find good sentence pairs. Evaluation shows that our alignment method outperforms state-of-the-art systems developed for the same task.

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In this paper, we present approaches used in text summarization, showing how they can be adapted for speech summarization and where they fall short. Informal style and apparent lack of structure in speech mean that the typical approaches used for text summarization must be extended for use with speech. We illustrate how features derived from speech can help determine summary content within two ongoing summarization projects at Columbia University. 1.

We present a novel method for task-based evalua- tion of summaries of scientific articles. The task we propose is a question-answering task, where the questions are about the relatedness of the current paper to prior research. This evaluation method is time-efficient with respect to material preparation and data collection, so that it is possible to test against many different baselines, something that is not usually feasible in evaluations by relevance decision. We use this methodology to evaluate the quality of summaries our system produces. These summaries are designed to describe the contribution of a scientific article in relation to other work. The re- sults show that this type of summary is indeed more useful than the baselines (random sentences, keyword lists and generic author-written summaries), and nearly as useful as the full texts.

Until now, Natural Language Processing (NLP) research development has mainly been conducted for the English speaking community. However, the European Union with its 25 member-states already involves 22 different official languages. As a consequence, multilinguality is certainly the most important challenge of this century for the European NLP community. In this paper, we show how the Centre for Human Language Technology and Bioinformatics has been dealing with the problem of multilinguality by proposing language independent systems instead of language tailored architectures. 1.

We address the problem of sentence alignment for monolingual corpora, a phenomenon distinct from alignment in parallel corpora. Aligning large comparable corpora automatically would provide a valuable resource for learning of text-totext rewriting rules. We incorporate context into the search for an optimal alignment in two complementary ways: learning rules for matching paragraphs using topic structure and further refining the matching through local alignment to find good sentence pairs. Evaluation shows that our alignment method outperforms state-of-the-art systems developed for the same task.