DiscSumm: A Discourse Based Saliency Model to Generate Abstract Summaries for Conversation

Summarization of large corpus of text is an important and hard problem. The amount of text information is increasing at an exponential rate which makes it consumption and organization difficult. This data also involves information obtained from transcription, chat along with traditional long form content such as books, periodicals, web pages etc. In this paper, we focus on long conversations obtained via transcription of multi speaker audio calls. Most of the traditional works text summarization focus on structured text summarization, however, we focus on multi turn conversation summarization. Many recent works perform model finetuning which improves the abstractive summarization by multi folds, it can still be improved further by incor-porating text into the encoder framework. Therefore, in this work we have studied the discourse-based saliency model that identifies the important segments within a text conversation and combines them adaptively to generate summary of the conversations. We demonstrate that the proposed method achieves state of the art re-sults on public benchmarks.