  Interleaved texts, where posts belonging to different threads occur in one
sequence, are a common occurrence, e.g., online chat conversations. To quickly
obtain an overview of such texts, existing systems first disentangle the posts
by threads and then extract summaries from those threads. The major issues with
such systems are error propagation and non-fluent summary. To address those, we
propose an end-to-end trainable hierarchical encoder-decoder system. We also
introduce a novel hierarchical attention mechanism which combines three levels
of information from an interleaved text, i.e, posts, phrases and words, and
implicitly disentangles the threads. We evaluated the proposed system on
multiple interleaved text datasets, and it out-performs a SOTA two-step system
by 20-40%.
