Machine Learning Approach to Evaluate MultiLingual Summaries
The present paper introduces a new MultiLing text summary evaluation method. This method relies on machine learning approach which operates by combining multiple features to build models that predict the human score (overall responsiveness) of a new summary. We have tried several single and {``}ensemble learning{''} classifiers to build the best model. We have experimented our method in summary level evaluation where we evaluate each text summary separately. The correlation between built models and human score is better than the correlation between baselines and manual score.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningEnsemble LearningSimilar Papers 제목 키워드 기반
mFACE: Multilingual Summarization with Factual Consistency Evaluation
Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from gene…
Abstractive Text SummarizationLessons from the Bible on Modern Topics: Low-Resource Multilingual Topic Model Evaluation
Multilingual topic models enable document analysis across languages through coherent multilingual summaries of the data. However, there is no standard and effective metric to evaluate the quality of multilingual topics. …
Topic ModelsEASY-M: Evaluation System for Multilingual Summarizers
Automatic text summarization aims at producing a shorter version of a document (or a document set). Evaluation of summarization quality is a challenging task. Because human evaluations are expensive and evaluators often …
Text SummarizationGenerating Extended and Multilingual Summaries with Pre-trained Transformers
Almost all summarisation methods and datasets focus on a single language and short summaries. We introduce a new dataset called WikinewsSum for English, German, French, Spanish, Portuguese, Polish, and Italian summarisat…
ArticlesOpenMSD: Towards Multilingual Scientific Documents Similarity Measurement
We develop and evaluate multilingual scientific documents similarity measurement models in this work. Such models can be used to find related works in different languages, which can help multilingual researchers find and…