| Challenge: | Existing approaches for text summarization are mostly automated, with limited space for human intervention and control. |
| Approach: | They propose a 2-phase summarization assistant that facilitates human-machine collaboration . it suggests possible content and generates a coherent summary from these selections . authors hope to improve the efficiency of the computer and human-involved approach . |
| Outcome: | The proposed summarization assistant is a 2-phase summarizing assistant . it suggests potential content and consolidates the output with visual mappings . the proposed system is available for free on youtube . |
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| Challenge: | despite advances in abstractive text summarization, the true performance and failure modes of modern neural models are not yet fully understood due to the black-box nature of neural models and unmanageable scale of recent datasets for manual analysis. |
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| Challenge: | Automated text summarization systems involve humans for preparing data or evaluating model performance, yet, there is no systematic understanding of human-AI interactions and how to design for them. |
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| Challenge: | Existing text summarization systems generate summaries in a single step, but are often inadequate due to the issue of hallucination and the lack of accuracy. |
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SummEval: Re-evaluating Summarization Evaluation (2021.tacl-1)
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SUMMARY WORKBENCH: Unifying Application and Evaluation of Text Summarization Models (2022.emnlp-demos)
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| Challenge: | Summary Workbench is a tool for developing and evaluating text summarization models. |
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| Challenge: | Existing summary evaluation methods rely on multiple model summaries to evaluate quality of summary outputs. |
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A Modular Approach for Multimodal Summarization of TV Shows (2024.acl-long)
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| Challenge: | In this paper, we address the task of summarizing television shows, which touches key areas in AI research. |
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