Abstract
Text generation applications such as machine translation and automatic summarization require an additional post-processing step to enhance readability and coherence of output texts. In this work, we identify a set of coherence features from different levels of discourse analysis. Features have either positive or negative input to the output coherence. We propose a new model that combines these features to produce more coherent summaries for our target application: extractive summarization. The model use a genetic algorithm to search for a better ordering of the extracted sentences to form output summaries. Experimentations on two datasets using an automatic coherence assessment measure show promising results.
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Oufaida, H., Blache, P., Nouali, O. (2019). A Coherence Model for Sentence Ordering. In: Métais, E., Meziane, F., Vadera, S., Sugumaran, V., Saraee, M. (eds) Natural Language Processing and Information Systems. NLDB 2019. Lecture Notes in Computer Science(), vol 11608. Springer, Cham. https://doi.org/10.1007/978-3-030-23281-8_21
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