Results of the summarizing process for the arXiv paper: 2007.12626v1

In their paper titled "SummEval: Re-evaluating Summarization Evaluation," authors Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Richard Socher, and Dragomir Radev highlight the scarcity of comprehensive and up-to-date studies on evaluation metrics for text summarization. They also point out the lack of consensus regarding evaluation protocols which hinders progress in this field. To address these shortcomings, the authors propose five dimensions to improve summarization evaluation methods. Firstly, they re-evaluate 12 automatic evaluation metrics using neural summarization model outputs alongside expert and crowd-sourced human annotations. This comprehensive and consistent approach allows for a more accurate assessment of these metrics. Secondly, the authors benchmark 23 recent summarization models using the aforementioned automatic evaluation metrics. By doing so they provide a reliable comparison between different models and their performance. Thirdly, they assemble the largest collection of summaries generated by models trained on the CNN/DailyMail news dataset and share it in a unified format. This dataset serves as a valuable resource for researchers working on text summarization tasks. Fourthly, the authors develop and share a toolkit that offers an extensible and unified API for evaluating summarization models across various automatic metrics. This toolkit enhances accessibility and facilitates further research in this area. Lastly, they compile the largest and most diverse collection of human judgments on model-generated summaries from the CNN/Daily Mail dataset. These judgments are annotated by both expert judges and crowd source workers providing valuable insights into how well these model-generated summaries align with human judgments. The authors hope that their work will contribute to promoting a more complete evaluation protocol for text summarization while advancing research in developing evaluation metrics that better correlate with human judgments.

• Scarcity of comprehensive and up-to-date studies on evaluation metrics for text summarization
• Lack of consensus regarding evaluation protocols
• Proposal of five dimensions to improve summarization evaluation methods
• Re-evaluation of 12 automatic evaluation metrics using neural summarization model outputs alongside expert and crowd-sourced human annotations
• Benchmarking 23 recent summarization models using automatic evaluation metrics for reliable comparison
• Assembly and sharing of the largest collection of summaries generated by models trained on the CNN/DailyMail news dataset in a unified format
• Development and sharing of a toolkit with an extensible and unified API for evaluating summarization models across various automatic metrics
• Compilation of the largest and most diverse collection of human judgments on model-generated summaries from the CNN/Daily Mail dataset, annotated by both expert judges and crowd source workers
• Contribution to promoting a more complete evaluation protocol for text summarization
• Advancement in research for developing evaluation metrics that better correlate with human judgments.

There are not many studies that have looked at how to measure how good a summary of text is. People don't agree on the best way to evaluate summaries. Some suggestions have been made to improve how we evaluate summaries. Researchers tested 12 different ways to evaluate summaries using a computer program and also by asking people for their opinions. They compared 23 different methods for making summaries to see which ones were the best. They collected a lot of summaries made by computers and put them all together in one place. They also made a tool that can be used to test how good a summary is using different methods. They asked lots of people what they thought about the computer-made summaries and used their answers to help make better evaluation methods." Definitions: - Scarcity: Not having enough of something. - Comprehensive: Including everything or almost everything. - Up-to-date: Current or recent. - Evaluation: The process of judging or assessing something. - Metrics: Measurements or standards used for evaluation. - Summarization: The act of making a shorter version of something, like a summary. - Consensus: When everyone agrees on something. - Protocols: Rules or guidelines. - Automatic: Done by a machine without human involvement. - Neural summarization model: A type of computer program that can make summaries based on patterns it has learned from examples. - Annotations: Notes or comments added to something, usually for explanation or clarification purposes.

SummEval: Re-evaluating Summarization Evaluation

Text summarization is a rapidly growing field of research, with many applications in areas such as natural language processing and machine learning. However, there has been a lack of comprehensive and up-to-date studies on evaluation metrics for text summarization. Additionally, there is no consensus regarding evaluation protocols which hinders progress in this field. To address these shortcomings, Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Richard Socher and Dragomir Radev propose five dimensions to improve summarization evaluation methods in their paper titled “SummEval: Re-evaluating Summarization Evaluation”.

Re-evaluation of Automatic Evaluation Metrics

The authors re-evaluate 12 automatic evaluation metrics using neural summarization model outputs alongside expert and crowd sourced human annotations. This comprehensive and consistent approach allows for a more accurate assessment of these metrics than previous approaches which used only one type of annotation or relied on manual comparison between summaries generated by different models.

Benchmarking Recent Summarization Models

The authors benchmark 23 recent summarization models using the aforementioned automatic evaluation metrics to provide a reliable comparison between different models and their performance. This helps researchers identify the best performing models for various tasks while also highlighting potential areas where further improvement can be made.

Assembling Summary Dataset

The authors assemble the largest collection of summaries generated by models trained on the CNN/DailyMail news dataset and share it in a unified format. This dataset serves as a valuable resource for researchers working on text summarization tasks as it provides access to high quality summary data that can be used to evaluate new methods or compare existing ones against each other without having to generate new summaries from scratch every time.

Developing Toolkit For Evaluating Summarizations

The authors develop and share a toolkit that offers an extensible and unified API for evaluating summarization models across various automatic metrics including BLEU score, ROUGE score etc.. This toolkit enhances accessibility and facilitates further research in this area by providing an easy way to evaluate multiple summary generation algorithms at once without having to write custom code each time.

Compiling Human Judgments On Model Generated Summaries

Lastly, they compile the largest and most diverse collection of human judgments on model generated summaries from the CNN/Daily Mail dataset annotated by both expert judges as well as crowd source workers providing valuable insights into how well these model generated summaries align with human judgments .This data set will help researchers better understand how humans perceive machine generated summaries compared with those written by humans themselves .

In conclusion ,the work done by Fabbri et al provides an important step forward towards developing more comprehensive evaluation protocols for text summarisation while advancing research into creating better correlation between automated evaluations scores with human judgments . Their proposed framework offers improved accuracy when assessing automatic evaluation metrics , benchmarks recent summarisation models , assembles large datasets containing model generated summaries along with corresponding human annotations , develops toolsets making it easier to evaluate multiple algorithms at once ,and compiles diverse collections of human judgements all under one roof . All these contributions are invaluable resources that will help drive future advancements within this field .