DeepAI AI Chat
Log In Sign Up

Factual Error Correction for Abstractive Summaries Using Entity Retrieval

04/18/2022
by   Hwanhee Lee, et al.
adobe
Seoul National University
0

Despite the recent advancements in abstractive summarization systems leveraged from large-scale datasets and pre-trained language models, the factual correctness of the summary is still insufficient. One line of trials to mitigate this problem is to include a post-editing process that can detect and correct factual errors in the summary. In building such a post-editing system, it is strongly required that 1) the process has a high success rate and interpretability and 2) has a fast running time. Previous approaches focus on regeneration of the summary using the autoregressive models, which lack interpretability and require high computing resources. In this paper, we propose an efficient factual error correction system RFEC based on entities retrieval post-editing process. RFEC first retrieves the evidence sentences from the original document by comparing the sentences with the target summary. This approach greatly reduces the length of text for a system to analyze. Next, RFEC detects the entity-level errors in the summaries by considering the evidence sentences and substitutes the wrong entities with the accurate entities from the evidence sentences. Experimental results show that our proposed error correction system shows more competitive performance than baseline methods in correcting the factual errors with a much faster speed.

READ FULL TEXT

page 1

page 2

page 3

page 4

10/17/2020

Factual Error Correction for Abstractive Summarization Models

Neural abstractive summarization systems have achieved promising progres...
11/11/2022

Improving Factual Consistency in Summarization with Compression-Based Post-Editing

State-of-the-art summarization models still struggle to be factually con...
09/19/2021

CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization

We study generating abstractive summaries that are faithful and factuall...
11/05/2021

A Syntax-Guided Grammatical Error Correction Model with Dependency Tree Correction

Grammatical Error Correction (GEC) is a task of detecting and correcting...
11/22/2022

Converge to the Truth: Factual Error Correction via Iterative Constrained Editing

Given a possibly false claim sentence, how can we automatically correct ...
10/21/2019

Diamonds in the Rough: Generating Fluent Sentences from Early-Stage Drafts for Academic Writing Assistance

The writing process consists of several stages such as drafting, revisin...