# Discourse Understanding and Factual Consistency in Abstractive Summarization

## TL;DR

This paper presents Co-opNet, a transformer-based framework that improves abstractive summarization by ensuring factual consistency and narrative coherence through a generator-discriminator architecture, evaluated on scientific papers.

## Contribution

The paper introduces Co-opNet, a novel generator-discriminator model that enhances factual accuracy and coherence in abstractive summaries, addressing hallucination and coherence issues.

## Key findings

- Co-opNet significantly improves global coherence over baselines.
- Automatic and human evaluations confirm better factual consistency.
- Discriminator objectives effectively capture coherence aspects.

## Abstract

We introduce a general framework for abstractive summarization with factual consistency and distinct modeling of the narrative flow in an output summary. Our work addresses current limitations of models for abstractive summarization that often hallucinate information or generate summaries with coherence issues. To generate abstractive summaries with factual consistency and narrative flow, we propose Cooperative Generator -- Discriminator Networks (Co-opNet), a novel transformer-based framework where a generator works with a discriminator architecture to compose coherent long-form summaries. We explore four different discriminator objectives which each capture a different aspect of coherence, including whether salient spans of generated abstracts are hallucinated or appear in the input context, and the likelihood of sentence adjacency in generated abstracts. We measure the ability of Co-opNet to learn these objectives with arXiv scientific papers, using the abstracts as a proxy for gold long-form scientific article summaries. Empirical results from automatic and human evaluations demonstrate that Co-opNet learns to summarize with considerably improved global coherence compared to competitive baselines.