01.10.2020 · Reference-based metrics such as ROUGE or BERTScore evaluate the content quality of a summary by comparing the summary to a reference. Ideally, this comparison should measure the summary's information quality by calculating how much information the …
11.10.2021 · （1）In machine translation, BERTSCORE shows stronger system-level and segment-level correlations with human judgments than existing metrics on multiple common benchmarks. （2）BERTSCORE is well-correlated with human annotators for image captioning, surpassing SPICE.
BERTScore (Zhang et al., 2019) is a recently proposed evaluation metric. Similar to ROUGE score, it computes a similarity score for each token in the generated ...
Improving Neural Abstractive Summarization via Reinforcement Learning with BERTScore Yuhui Zhang, Ruocheng Wang, Zhengping Zhou Department of Computer Science Stanford University [yuhuiz, rcwang, zpzhou]@stanford.edu 1 Introduction Abstractive summarization aims to paraphrase long text with a short summary. While it is a common practice to train
... human judgments for natural language generation (Table 1), can we use reinforcement learning with BERTScore to improve neural abstractive summarization?
Mar 12, 2020 · Here is how BERT_Sum_Abs performs on the standard summarization datasets: CNN and Daily Mail that are commonly used in benchmarks. The evaluation metric is known as ROGUE F1 score—. Based on Text Summarization with Pretrained Encoders by Yang Liu and Mirella Lapata. Results show that BERT_Sum_Abs outperforms most non-Transformer based models.
01.09.2021 · SummerTime - Text Summarization Toolkit for Non-experts. A library to help users choose appropriate summarization tools based on their specific tasks or needs. Includes models, evaluation metrics, and datasets. The library architecture is as follows:
We examine eight metrics that measure the agree- ment between two texts, in our case, between the system summary and reference summary. BERTScore (BScore) ...
02.01.2022 · %0 Conference Proceedings %T QuestEval: Summarization Asks for Fact-based Evaluation %A Scialom, Thomas %A Dray, Paul-Alexis %A Lamprier, Sylvain %A Piwowarski, Benjamin %A Staiano, Jacopo %A Wang, Alex %A Gallinari, Patrick %S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing %D 2021 %8 nov %I …
We propose BERTScore, an automatic evaluation metric for text generation. Analogously to common metrics, BERTScore computes a similarity score for each token in the candidate sentence with each token in the reference sentence. However, instead of exact matches, we compute token similarity using contextual embeddings.
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference sentences by cosine similarity. It has been ...
Abstract: We propose BERTScore, an automatic evaluation metric for text generation. Analogously to common metrics, BERTScore computes a similarity score for ...
We propose BERTScore, an automatic evaluation metric for text generation. Analogously to common metrics, BERTScore computes a similarity score for each ...
Jun 07, 2019 · The summarization model could be of two types: Extractive Summarization — Is akin to using a highlighter. We select sub segments of text from the original text that would create a good summary; Abstractive Summarization — Is akin to writing with a pen. Summary is created to extract the gist and could use words not in the original text.