Task Families: A New Approach to Improve AI Text Summarization

What is it about?

AI models for text summarization can benefit from learning multiple related tasks. Our research explores how grouping these tasks into "families" (like reading comprehension or language inference) affects the AI's summarization ability. We tested three strategies for training AI with these task families: sequential, simultaneous, and continual learning. Our findings show that certain combinations of task families, particularly advanced reading comprehension and natural language inference, significantly improve the AI's summarization performance. Interestingly, we discovered that the choice and combination of task families matter more than the specific training method used. This suggests that carefully selecting task families is crucial for enhancing AI text summarization capabilities. Our study highlights the importance of task families in developing more effective AI summarization tools.

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Photo by Max Chen on Unsplash

Photo by Max Chen on Unsplash