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Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization ...
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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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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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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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Capturing Speaker Incorrectness: Speaker-Focused Post-Correction for Abstractive Dialogue Summarization ...
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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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Abstract:
Many applications require generation of summaries tailored to the user’s information needs, i.e., their intent. Methods that express intent via explicit user queries fall short when query interpretation is subjective. Several datasets exist for summarization with objective intents where, for each document and intent (e.g., “weather”), a single summary suffices for all users. No datasets exist, however, for subjective intents (e.g., “interesting places”) where different users will provide different summaries. We present SUBSUME, the first dataset for evaluation of SUBjective SUMmary Extraction systems. SUBSUME contains 2,200 (document, intent, summary) triplets over 48 Wikipedia pages, with ten intents of varying subjectivity, provided by 103 individuals over Mechanical Turk. We demonstrate statistically that the intents in SUBSUME vary systematically in subjectivity. To indicate SUBSUME’s usefulness, we explore a collection of baseline algorithms for subjective extractive summarization and show that (i) as ...
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Keyword:
Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Summarization
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URL: https://underline.io/lecture/39824-subsume-a-dataset-for-subjective-summary-extraction-from-wikipedia-documents https://dx.doi.org/10.48448/rp8c-e676
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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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