SOTAVerified

Summary Refinement through Denoising

2019-07-25RANLP 2019Code Available0· sign in to hype

Nikola I. Nikolov, Alessandro Calmanovici, Richard H. R. Hahnloser

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

We propose a simple method for post-processing the outputs of a text summarization system in order to refine its overall quality. Our approach is to train text-to-text rewriting models to correct information redundancy errors that may arise during summarization. We train on synthetically generated noisy summaries, testing three different types of noise that introduce out-of-context information within each summary. When applied on top of extractive and abstractive summarization baselines, our summary denoising models yield metric improvements while reducing redundancy.

Tasks

Reproductions