Analysis of Deep Learning Models for Text Summarization of User Manuals

User manuals have an inconsistent structure with the data presented in multiple formats such as tables, images, etc. It makes processing them a challenging task as we need to account for these inconsistencies. In this work, we propose a pipeline for processing user manuals and analyzing abstractive model PEGASUS and extractive models XLNet, BERT, and GPT-2 for summarization of user manuals. To evaluate the models, we have generated extractive and abstractive datasets and used metrics such as hit ratio, overlap, and rouge score to compare the performance of the models. We observed that an abstractive model gives more human-like summaries compared to the extractive models which although have higher rouge scores, suffer in readability. The system utilizes automatic text summarization along with multiple methods to process user manuals and extract required information in a summarized manner.