11 citations · 16 across the 5 of their papers we have counts for
5 papers · 1 filter
Improving Factual Error Correction for Abstractive Summarization via Data Distillation and Conditional-generation Cloze
Yiyang Li, Lei Li, Dingxin Hu +4
Improving factual consistency in abstractive summarization has been a focus of current research. One promising approach is the post-editing method. However, previous works have yet…
Just ClozE! A Novel Framework for Evaluating the Factual Consistency Faster in Abstractive Summarization
Yiyang Li, Lei Li, Marina Litvak +4
The issue of factual consistency in abstractive summarization has received extensive attention in recent years, and the evaluation of factual consistency between summary and docume…
Subjective Bias in Abstractive Summarization
Lei Li, Wei Liu, Marina Litvak +4
Due to the subjectivity of the summarization, it is a good practice to have more than one gold summary for each training document. However, many modern large-scale abstractive summ…
Automated Discovery of Mathematical Definitions in Text with Deep Neural Networks
Natalia Vanetik, Marina Litvak, Sergey Shevchuk +1
Automatic definition extraction from texts is an important task that has numerous applications in several natural language processing fields such as summarization, analysis of scie…
In Conclusion Not Repetition: Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point Processes
Lei Li, Wei Liu, Marina Litvak +2
Various Seq2Seq learning models designed for machine translation were applied for abstractive summarization task recently. Despite these models provide high ROUGE scores, they are…