CIST System for CL SciSumm 2016 Shared Task
This paper introduces the methods and experiments applied in CIST system participating in the CLSciSumm 2016 Shared Task at BIRNDL 2016. We have participated in the TAC 2014 Biomedical Summarization Track, so we develop the system based on previous work. This time the domain is Computational Linguistics (CL) The training corpus contains 20 topics from Training Set 2016 and Development Set Apr8 published by CL SciSumm 2016. As to Task 1A and 1B, we mainly use rule based methods with various features of lexicons and similarities; meanwhile we also have tried the machine learning method of SVM. As to Task 2, hLDA topic model is adopted for content modeling, which provides us knowledge about sentence clustering (subtopic) and word distributions (abstractiveness) for summarization. We then combine hLDA knowledge with several other classical features using different weights and proportions to evaluate the sentences in the Reference Paper from its cited text spans. Finally we extract the representative sentences to generate a summary within 250 words.
BIRNDL@JCDL