The automation of text summarisation of biomedical publications is a pressing need due to the plethora of information available on-line. This paper explores the impact of several supervised machine learning approaches for extracting multi-document summaries for given queries. In particular, we compare classification and regression approaches for query-based extractive summarisation using data provided by the BioASQ Challenge. We tackled the problem of annotating sentences for training classification systems and show that a simple annotation approach outperforms regression-based summarisation.
Regression approaches for summarisation try to fit the predicted score of a sentence as close as possible to the target score instead of labelling the sentences. An early work using regression for summarisation is by using support vector regression (SVR). Support vector regression (SVR) has also been used in conjunction with other techniques like integer linear programming (ILP) for generating summaries @cite_14 and has achieved state-of-the-art results in comparison to other competitive extractive summarisers.
@cite_14
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We present a new method to generate extractive multi-document summaries. The method uses Integer Linear Programming to jointly maximize the importance of the sentences it includes in the summary and their diversity, without exceeding a maximum allowed summary length. To obtain an importance score for each sentence, it uses a Support Vector Regression model trained on human-authored summaries, whereas the diversity of the selected sentences is measured as the number of distinct word bigrams in the resulting summary. Experimental results on widely used benchmarks show that our method achieves state of the art results, when compared to competitive extractive summarizers, while being computationally efficient as well.