Hey @yjernite, the primary challenge as you mentioned is to identify the speaker and hence interpret the structure. The dataset is somewhat similar to (SAMsum corpus - https://arxiv.org/src/1911.12237v2/anc/corpus.7z 13).
The following are the key points that might help -

The summaries are there.
The chats are similar to normal texts exchanged between two users.
There are around 15K-20K training examples.
Currently, the speaker is represented as is. (Based on Name)

Kindly suggest the improvements for better implementation of abstractive summarization. Following are my key queries -

Is there any preferred model for chat summarization?
What might be the pre-processing steps for improvement in performance?
How should speakers be represented as it was found that the contexts might be changed because of a speaker name being present in a sentence (ambiguity increased) ?

Any suggestion would be of great help !