United States

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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NAACL 2021

Modern summarization models generate highly fluent but often factually unreliable outputs. This motivated a surge of metrics attempting to measure the factuality of automatically generated summaries. Due to the lack of common benchmarks, these metrics cannot be compared. Moreover, all these methods treat factuality as a binary concept and fail to provide deeper insights into the kinds of inconsistencies made by different systems. To address these limitations, we devise a typology of factual errors and use it to collect human annotations of generated summaries from state-of-the-art summarization systems for the CNN/DM and XSum datasets. Through these annotations, we identify the proportion of different categories of factual errors in various summarization models and benchmark factuality metrics, showing their correlation with human judgment as well as their specific strengths and weaknesses.

Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics

technical paper

Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval

A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

Differentiable Open-Ended Commonsense Reasoning

ESC: Redesigning WSD with Extractive Sense Comprehension

Scalable and Interpretable Semantic Change Detection

Constructing Taxonomies from Pretrained Language Models

Event Representation with Sequential, Semi-Supervised Discrete Variables

Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters

Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization

What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization

https://d3smihljt9218e.cloudfront.net/transcript/13947/subtitle/2783df2e5138d08de5afed1b83a52dcf.vtt

English (automatic)

Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics

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Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrieval

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