Interactive Editing for Text Summarization
Yujia Xie Xun Wang Si-Qing Chen Wayne Xiong Pengcheng He
Summarizing lengthy documents is a common and essential task in our daily
lives. Although recent advancements in neural summarization models can assist
in crafting general-purpose summaries, human writers often have specific
requirements that call for a more customized approach. To address this need, we
introduce REVISE (Refinement and Editing via Iterative Summarization
Enhancement), an innovative framework designed to facilitate iterative editing
and refinement of draft summaries by human writers. Within our framework,
writers can effortlessly modify unsatisfactory segments at any location or
length and provide optional starting phrases -- our system will generate
coherent alternatives that seamlessly integrate with the existing summary. At
its core, REVISE incorporates a modified fill-in-the-middle model with the
encoder-decoder architecture while developing novel evaluation metrics tailored
for the summarization task. In essence, our framework empowers users to create
high-quality, personalized summaries by effectively harnessing both human
expertise and AI capabilities, ultimately transforming the summarization
process into a truly collaborative and adaptive experience.