  Although some recent works show potential complementarity among different
state-of-the-art systems, few works try to investigate this problem in text
summarization. Researchers in other areas commonly refer to the techniques of
reranking or stacking to approach this problem. In this work, we highlight
several limitations of previous methods, which motivates us to present a new
framework Refactor that provides a unified view of text summarization and
summaries combination. Experimentally, we perform a comprehensive evaluation
that involves twenty-two base systems, four datasets, and three different
application scenarios. Besides new state-of-the-art results on CNN/DailyMail
dataset (46.18 ROUGE-1), we also elaborate on how our proposed method addresses
the limitations of the traditional methods and the effectiveness of the
Refactor model sheds light on insight for performance improvement. Our system
can be directly used by other researchers as an off-the-shelf tool to achieve
further performance improvements. We open-source all the code and provide a
convenient interface to use it:
https://github.com/yixinL7/Refactoring-Summarization. We have also made the
demo of this work available at:
http://explainaboard.nlpedia.ai/leaderboard/task-summ/index.php.
