# A Hierarchical Decoder with Three-level Hierarchical Attention to Generate Abstractive Summaries of Interleaved Texts

## TL;DR

This paper introduces an end-to-end hierarchical encoder-decoder with a three-level attention mechanism for abstractive summarization of interleaved texts, effectively reducing error propagation and improving fluency.

## Contribution

It presents a novel hierarchical attention mechanism and an integrated model that outperforms existing two-step systems in summarizing interleaved texts.

## Key findings

- Outperforms state-of-the-art two-step systems by 20-40%.
- Effectively disentangles threads without explicit separation.
- Enhances summary fluency and coherence.

## Abstract

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%.