In this paper, we report on our experiments in building a summarization system for generating summaries from annual reports. We adopt an “extractive” summarization approach in our hybrid system combining neural networks and rules-based algorithms with the expectation that such a system may capture key sentences or paragraphs from the data. A rules-based TOC (Table Of Contents) extraction and a binary classifier of narrative section titles are main components of our system allowing to identify narrative sections and best candidates for extracting final summaries. As result, we propose one to three summaries per document according to the classification score of narrative section titles.
Data Anonymization for Requirements Quality Analysis: a Reproducible Automatic Error Detection Task
Juyeon Kang | Jungyeul Park
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)