Abstract
In this paper we present an approach to document summarization based on unsupervised techniques. We study the adequacy of these techniques to the problem of documents in which many topics of different duration are present, in our case the transcriptions of Spanish TV programs. The paper compares a classical Latent Semantic Analysis approach to a new proposal based on Latent Dirichlet Allocation. It is also studied the application of the summarization process to the different segments obtained in a previous process of topic segmentation. The topic segmentation is performed by considering distances between paragraphs, that are represented by means of continuous vectors obtained from the words contained in them. Experiments on some TV programs of political and miscellaneous news have been performed.
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Acknowledgments
This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC: Affective Multimedia Analytics with Inclusive and Natural Communication (TIN2017-85854-C4-2-R).
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Canora, M., García-Granada, F., Sanchis, E., Segarra, E. (2018). An Approach to Automatic Summarization of Television Programs. In: Karpov, A., Jokisch, O., Potapova, R. (eds) Speech and Computer. SPECOM 2018. Lecture Notes in Computer Science(), vol 11096. Springer, Cham. https://doi.org/10.1007/978-3-319-99579-3_10
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