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

TL;DR

This survey treats controllable text summarization (CTS) as a conditional generation problem where user-defined controllable attributes (CAs) steer the produced summaries. It formalizes CTS with $P(S|D,C)=\prod_{i}^{k} P(S_i|d_i,C)$ and catalogs 10 CA categories (Length, Style, Coverage, Entity, Structure, Abstractivity, Salience, Role, Diversity, Topic), organizing them into a coherent taxonomy. The authors analyze 61 CTS papers across generic and derived datasets, including human-annotated corpora like GranDUC, Multi-LexSum, EntSUM/EntSUMV2, NEWTS, CSDS, MReD, and MACSUM, and review methods ranging from input/decoder modifications to RL and mixture-of-experts approaches. They evaluate CTS with a mix of automatic metrics (ROUGE, BLEU, BERTScore, MoveScore, task-specific indicators) and human judgments across multiple attributes, highlighting critical needs for standardized CA-specific metrics, explainability, reproducibility, and multilingual/multimodal CTS. The work provides a structured roadmap for CTS research and maintains a public GitHub resource to track developments and datasets, guiding future benchmarks, methods, and evaluation practices.

Abstract

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. Despite a growing corpus of controllable summarization research, there is no comprehensive survey available that thoroughly explores the diverse controllable 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 attributes according to their shared characteristics and objectives, and present a thorough examination of existing datasets and methods within each category. Moreover, based on our findings, we uncover limitations and research gaps, while also exploring potential solutions and future directions for CTS. We release our detailed analysis of CTS papers at https://github.com/ashokurlana/controllable_text_summarization_survey.

Figures (6)

• Figure 1: Number of controllable text summarization publications for various attributes.
• Figure 2: Year-wise papers published in CTS to handle various controllable attributes.
• Figure 3: Various training approaches utilized to perform CTS tasks.
• Figure 4: Domains utilized in CTS; most of the existing CTS tasks build on news domain data due to ease in accessibility.
• Figure 5: Type of models used in CTS; the majority of the models fall under standard sequence-to-sequence architecture.