A topic-based sentence representation for extractive text summarization
Nikolaos Gialitsis | Nikiforos Pittaras | Panagiotis Stamatopoulos
Proceedings of the Workshop MultiLing 2019: Summarization Across Languages, Genres and Sources
In this study, we examine the effect of probabilistic topic model-based word representations, on sentence-based extractive summarization. We formulate the task of summary extraction as a binary classification problem, and we test a variety of machine learning algorithms, exploring a range of different settings. An wide experimental evaluation on the MultiLing 2015 MSS dataset illustrates that topic-based representations can prove beneficial to the extractive summarization process in terms of F1, ROUGE-L and ROUGE-W scores, compared to a TF-IDF baseline, with QDA-based analysis providing the best results.