Controllable Summarization with Constrained Markov Decision Process

• URL: http://arxiv.org/abs/2108.03405v1
• Date: Sat, 7 Aug 2021 09:12:53 GMT
• Title: Controllable Summarization with Constrained Markov Decision Process
• Authors: Hou Pong Chan, Lu Wang, Irwin King
• Abstract summary: We study controllable text summarization which allows users to gain control on a particular attribute. We propose a novel training framework based on Constrained Markov Decision Process (CMDP) Our framework can be applied to control important attributes of summarization, including length, covered entities, and abstractiveness.
• Score: 50.04321779376415
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Guide-to-Explain for Controllable Summarization [11.904090197598505]
  controllable summarization with large language models (LLMs) remains underexplored. We propose a guide-to-explain framework (GTE) for controllable summarization. Our framework enables the model to identify misaligned attributes in the initial draft and guides it in explaining errors in the previous output.
  arXiv  Detail & Related papers  (2024-11-19T12:36:02Z)
• From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification [70.08146540745877]
  We investigate common constraints in NLP tasks, categorize them into three classes based on the types of their arguments. We propose a unified framework, ACT (Aligning to ConsTraints), to automatically produce supervision signals for user alignment with constraints.
  arXiv  Detail & Related papers  (2024-03-10T22:14:54Z)
• PromptSum: Parameter-Efficient Controllable Abstractive Summarization [4.145362426026615]
  We introduce PromptSum, a method combining PT with a multi-task objective and discrete entity prompts for abstractive summarization. Our model competitive ROUGE results on popular abstractive summarization benchmarks coupled with a strong level of controllability through entities.
  arXiv  Detail & Related papers  (2023-08-06T13:54:14Z)
• MACSum: Controllable Summarization with Mixed Attributes [56.685735509260276]
  MACSum is the first human-annotated summarization dataset for controlling mixed attributes. We propose two simple and effective parameter-efficient approaches for the new task of mixed controllable summarization.
  arXiv  Detail & Related papers  (2022-11-09T17:17:37Z)
• EntSUM: A Data Set for Entity-Centric Summarization [27.845014142019917]
  Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences. We introduce a human-annotated data setSUM for controllable summarization with a focus on named entities as the aspects to control.
  arXiv  Detail & Related papers  (2022-04-05T13:45:54Z)
• Abstractive Query Focused Summarization with Query-Free Resources [60.468323530248945]
  In this work, we consider the problem of leveraging only generic summarization resources to build an abstractive QFS system. We propose Marge, a Masked ROUGE Regression framework composed of a novel unified representation for summaries and queries. Despite learning from minimal supervision, our system achieves state-of-the-art results in the distantly supervised setting.
  arXiv  Detail & Related papers  (2020-12-29T14:39:35Z)
• CTRLsum: Towards Generic Controllable Text Summarization [54.69190421411766]
  We presentsum, a novel framework for controllable summarization. Our approach enables users to control multiple aspects of generated summaries by interacting with the summarization system. Using a single unified model,sum is able to achieve a broad scope of summary manipulation at inference time.
  arXiv  Detail & Related papers  (2020-12-08T08:54:36Z)
• Constrained Abstractive Summarization: Preserving Factual Consistency with Constrained Generation [93.87095877617968]
  We propose Constrained Abstractive Summarization (CAS), a general setup that preserves the factual consistency of abstractive summarization. We adopt lexically constrained decoding, a technique generally applicable to autoregressive generative models, to fulfill CAS. We observe up to 13.8 ROUGE-2 gains when only one manual constraint is used in interactive summarization.
  arXiv  Detail & Related papers  (2020-10-24T00:27:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.