论文标题
FFCI：可解释自动评估摘要的框架
FFCI: A Framework for Interpretable Automatic Evaluation of Summarization
论文作者
论文摘要
在本文中，我们提出了FFCI，这是一个构成四个要素的细粒度摘要评估框架：忠诚度（事实与来源的事实一致性），焦点（相对于参考的摘要内容的精确），覆盖范围（召回相对于参考文献的摘要内容）和内部式相干性（文档插入式置换）。我们构建了一个新型数据集，以基于评估指标和基于模型的评估方法的交叉比较来评估FFCI的四个维度中的每个维度中的每个维度，以评估FFCI的四个维度中的每个维度，包括问题答案（QA）方法（包括语义文本相似性（STS），下一个定期（NSpspeors）（nsp）和score scores corors corors scores。然后，我们将开发的指标应用于评估两个数据集的广泛汇总模型，并有一些令人惊讶的发现。
In this paper, we propose FFCI, a framework for fine-grained summarization evaluation that comprises four elements: faithfulness (degree of factual consistency with the source), focus (precision of summary content relative to the reference), coverage (recall of summary content relative to the reference), and inter-sentential coherence (document fluency between adjacent sentences). We construct a novel dataset for focus, coverage, and inter-sentential coherence, and develop automatic methods for evaluating each of the four dimensions of FFCI based on cross-comparison of evaluation metrics and model-based evaluation methods, including question answering (QA) approaches, semantic textual similarity (STS), next-sentence prediction (NSP), and scores derived from 19 pre-trained language models. We then apply the developed metrics in evaluating a broad range of summarization models across two datasets, with some surprising findings.
