In this paper we show that information from citing papers can help perform extractive summarisation of medical publications, especially when the amount of text available for development is limited. We used the data of the TAC 2014 biomedical summarisation task. We report several methods to find the reference paper sentences that best match the citation text from the citing papers (“citances”). ...

We propose and develop a simple and efficient algorithm for generating extractive multi-document summaries and show that this algorithm exhibits stateof-the-art or near state-of-the-art performance on two Document Understanding Conference datasets and two Text Analysis Conference datasets. Our results show that algorithms using simple features and computationally efficient methods are competiti...

This paper studies the problem of automatically extracting a short title from a manually written longer description of E-commerce products for display on mobile devices. It is a new extractive summarization problem on short text inputs, for which we propose a feature-enriched network model, combining three different categories of features in parallel. Experimental results show that our framewor...

The problem of extractive text summarization for a collection of documents is defined as selecting a small subset of sentences so the contents and meaning of the original document set are preserved in the best possible way. In this paper we present a new model for the problem of extractive summarization, where we strive to obtain a summary that preserves the information coverage as much as poss...

While sentence extraction as an approach to summarization has been shown to work in documents of certain genres, because of the conversational nature of email communication, sentence extraction may not result in a coherent summary. In this paper, we present our work on augmenting extractive summaries of threads of email conversations with automatically detected question-answer pairs. We compare...

Extractive methods work by selecting a subset of existing words, phrases, or sentences in the original text to form the summary. In contrast, abstractivemethods build an internal semantic representation and then use natural language generation techniques to create a summary that is closer to what a human might generate. Such a summary might contain words not explicitly present in the original. ...

We present an approach for extractive, query-focused, singledocument summarisation of medical text. Our approach utilises a combination of target-sentence-specific and target-sentence-independent statistics derived from a corpus specialised for summarisation in the medical domain. We incorporate domain knowledge via the application of multiple domain-specific features, and we customise the answ...

There are known biocatalytic pathways to produce many common fuels and petrochemicals. These biocatalysts can be either enzymes or living microorganisms. The challenge is to produce these fuels and chemicals efficiently and economically. Critical parameters include feedstock Costs, yield, rate and downstream processing. Here we will examine several immobilized biocatalyst reactor designs that w...

In this paper we address two key challenges for extractive multi-document summarization: the search problem of finding the best scoring summary and the training problem of learning the best model parameters. We propose an A* search algorithm to find the best extractive summary up to a given length, which is both optimal and efficient to run. Further, we propose a discriminative training algorit...

The rising demand for consistent, high quality, as well as high density wood species has raised interest in diff erent wood modifi cation techniques. One modifi cation method is wood densifi-cation, where solid bulk wood can be compressed, following softening, until its density reaches that of the cell wall (1.50 g/cm 3). It is well known that many of the properties of solid wood correlate with...