Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization
Sentence summarization shortens given texts while maintaining core contents of the texts. Unsupervised approaches have been studied to summarize texts without human-written summaries. However, recent unsupervised models are extractive, which remove words from texts and thus they are less flexible than abstractive summarization. In this work, we devise an abstractive model based on reinforcement learning without ground-truth summaries. We formulate the unsupervised summarization based on the Markov decision process with rewards representing the summary quality. To further enhance the summary quality, we develop a multi-summary learning mechanism that generates multiple summaries with varying lengths for a given text, while making the summaries mutually enhance each other. Experimental results show that the proposed model substantially outperforms both abstractive and extractive models, yet frequently generating new words not contained in input texts.
Code (1)
Tasks
Abstractive Text Summarizationreinforcement-learningReinforcement Learning (RL)SentenceSentence SummarizationUnsupervised Sentence SummarizationSimilar Papers 제목 키워드 기반
Self-Attention Recurrent Summarization Network with Reinforcement Learning for Video Summarization Task
With the exponential growth of video data, video summarization techniques are urgently needed for reducing people’s efforts in the videos' content exploration by generating succinct but informative summaries from origina…
reinforcement-learningReinforcement LearningSupervised Video SummarizationUnsupervised Video Summarization+1Multi-Dimensional Optimization for Text Summarization via Reinforcement Learning
The evaluation of summary quality encompasses diverse dimensions such as consistency, coherence, relevance, and fluency. However, existing summarization methods often target a specific dimension, facing challenges in gen…
Multi-Objective Reinforcement LearningMulti-Task Learningreinforcement-learningReinforcement Learning+1Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning
Video summarization aims at generating concise video summaries from the lengthy videos, to achieve better user watching experience. Due to the subjectivity, purely supervised methods for video summarization may bring the…
Decision MakingMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+3Generating and Evaluating Summaries for Partial Email Threads: Conversational Bayesian Surprise and Silver Standards
We define and motivate the problem of summarizing partial email threads. This problem introduces the challenge of generating reference summaries for partial threads when human annotation is only available for the threads…
Read what you need: Controllable Aspect-based Opinion Summarization of Tourist Reviews
Manually extracting relevant aspects and opinions from large volumes of user-generated text is a time-consuming process. Summaries, on the other hand, help readers with limited time budgets to quickly consume the key ide…
Document SummarizationMulti-Document SummarizationOpinion Summarization