Generic text summarization approaches often fail to address the specific
intent and needs of individual users. Recently, scholarly attention has turned
to the development of summarization methods that are more closely tailored and
controlled to align with specific objectives and user needs. While a growing
corpus of research is devoted towards a more controllable summarization, there
is no comprehensive survey available that thoroughly explores the diverse
controllable aspects or attributes employed in this context, delves into the
associated challenges, and investigates the existing solutions. In this survey,
we formalize the Controllable Text Summarization (CTS) task, categorize
controllable aspects according to their shared characteristics and objectives,
and present a thorough examination of existing methods and datasets within each
category. Moreover, based on our findings, we uncover limitations and research
gaps, while also delving into potential solutions and future directions for
CTS.

总结特定用户的需要和意图，关注于开发更贴合特定目标和用户需求的文本摘要方法。最近的学术关注点转向了更可控的摘要方法的发展，但缺乏全面的调查来深入探索在这一背景下所使用的各种可控性方面或属性，并研究现有的解决方案。在本调查中，我们形式化了可控文本摘要任务，根据它们的共享特征和目标将可控性方面进行分类，并对每个类别中的现有方法和数据集进行了全面的研究。此外，根据我们的调查结果，我们发现了一些限制和研究空白，同时也探讨了 CTS 的潜在解决方案和未来发展方向。

可控文本摘要：挑战、方法和前景的揭示 -- 一项综述

Controllable Text Summarization: Unraveling Challenges, Approaches, and  Prospects -- A Survey

We study controllable text summarization which allows users to gain control
on a particular attribute (e.g., length limit) of the generated summaries. In
this work, we propose a novel training framework based on Constrained Markov
Decision Process (CMDP), which conveniently includes a reward function along
with a set of constraints, to facilitate better summarization control. The
reward function encourages the generation to resemble the human-written
reference, while the constraints are used to explicitly prevent the generated
summaries from violating user-imposed requirements. Our framework can be
applied to control important attributes of summarization, including length,
covered entities, and abstractiveness, as we devise specific constraints for
each of these aspects. Extensive experiments on popular benchmarks show that
our CMDP framework helps generate informative summaries while complying with a
given attribute's requirement.

本研究通过引入基于约束马尔可夫决策过程（CMDP）的训练框架，允许用户对生成的摘要进行特定属性（例如长度限制）的控制，该框架旨在通过奖励函数和一组约束来提高摘要生成的可控性。实验结果表明，我们的 CMDP 框架有助于生成具有信息量的摘要，并符合用户对属性的要求。