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Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization ...
Abstract: We consider the problem of topic-focused ab- stractive summarization, where the goal is to generate an abstractive summary focused on a particular topic, a phrase of one or multiple words. We hypothesize that the task of generating topic-focused summaries can be improved by showing the model what it must not focus on. We introduce a deep reinforcement learning approach to topic-focused abstractive summarization, trained on rewards with a novel negative example baseline. We define the input in this problem as the source text preceded by the topic. We adapt the CNN-Daily Mail and New York Times summarization datasets for this task. We then show through experiments on existing rewards that the use of a negative example baseline can outperform the use of a self-critical baseline, in ROUGE, BERTSCORE, and human evaluation metrics. ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Summarization
URL: https://underline.io/lecture/39831-rewards-with-negative-examples-for-reinforced-topic-focused-abstractive-summarization
https://dx.doi.org/10.48448/xfsb-0734
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2
Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining ...
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HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization ...
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CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization ...
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5
Weakly supervised discourse segmentation for multiparty oral conversations ...
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Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization ...
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Evaluation of Summarization Systems across Gender, Age, and Race ...
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Controllable Neural Dialogue Summarization with Personal Named Entity Planning ...
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CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization ...
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A Thorough Evaluation of Task-Specific Pretraining for Summarization ...
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Effective Sequence-to-Sequence Dialogue State Tracking ...
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Context or No Context? A preliminary exploration of human-in-the-loop approach for Incremental Temporal Summarization in meetings ...
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Exploring Multitask Learning for Low-Resource Abstractive Summarization ...
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14
Capturing Speaker Incorrectness: Speaker-Focused Post-Correction for Abstractive Dialogue Summarization ...
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15
Narrative Embedding: Re-Contextualization Through Attention ...
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TWEETSUMM - A Dialog Summarization Dataset for Customer Service ...
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SUBSUME: A Dataset for Subjective Summary Extraction from Wikipedia Documents ...
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AUTOSUMM: Automatic Model Creation for Text Summarization ...
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A Statistical Analysis of Summarization Evaluation Metrics Using Resampling Methods ...
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20
Retrieval Augmented Code Generation and Summarization ...
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