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1
Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization ...
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2
Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining ...
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3
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization ...
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4
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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6
Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive Summarization ...
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7
Evaluation of Summarization Systems across Gender, Age, and Race ...
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8
Controllable Neural Dialogue Summarization with Personal Named Entity Planning ...
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9
CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization ...
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10
A Thorough Evaluation of Task-Specific Pretraining for Summarization ...
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11
Effective Sequence-to-Sequence Dialogue State Tracking ...
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12
Context or No Context? A preliminary exploration of human-in-the-loop approach for Incremental Temporal Summarization in meetings ...
Abstract: Incremental meeting temporal summarization, summarizing relevant information of partial multi-party meeting dialogue, is emerging as the next challenge in summarization research. Here we examine the extent to which human abstractive summaries of the preceding increments (context) can be combined with extractive meeting dialogue to generate abstractive summaries. We find that previous context improves ROUGE scores. Our findings further suggest that contexts begin to outweigh the dialogue. Using keyphrase extraction and semantic role labeling (SRL), we find that SRL captures relevant information without overwhelming the the model architecture. By compressing the previous contexts by ~70%, we achieve better ROUGE scores over our baseline models. Collectively, these results suggest that context matters, as does the way in which context is presented to the model. ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Summarization
URL: https://underline.io/lecture/39827-context-or-no-contextquestion-a-preliminary-exploration-of-human-in-the-loop-approach-for-incremental-temporal-summarization-in-meetings
https://dx.doi.org/10.48448/nkf7-1c08
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13
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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16
TWEETSUMM - A Dialog Summarization Dataset for Customer Service ...
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17
SUBSUME: A Dataset for Subjective Summary Extraction from Wikipedia Documents ...
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18
AUTOSUMM: Automatic Model Creation for Text Summarization ...
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19
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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