Question Answering as an Automatic Evaluation Metric for News Article Summarization (N19-1)
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| Challenge: | Recent work on summarization and headline generation focuses on maximizing ROUGE scores. |
| Approach: | They propose an extrinsic evaluation metric that maximizes ROUGE scores for automatic summarization and headline generation. |
| Outcome: | The proposed model maximizes ROUGE scores while increasing competitive results. |
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SummEval: Re-evaluating Summarization Evaluation (2021.tacl-1)
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| Challenge: | Effective summarisation evaluation metrics enable researchers and practitioners to compare different summarization systems efficiently. |
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| Challenge: | Existing text overlap based evaluation metrics are limited to matching tokens, either lexically or via embeddings. |
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Guiding Extractive Summarization with Question-Answering Rewards (N19-1)
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| Challenge: | a primary challenge faced by extractive summarization systems is the lack of annotated data. |
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| Challenge: | Existing methods to evaluate text summarization tasks using ROUGE have been criticized for lack of semantic understanding. |
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| Challenge: | a recent study focused on intrinsic evaluation, which assesses the quality of summaries, e.g. coherence, fluency, and informativeness, but it focused on task-based extrinsic evaluation to determine the usefulness of summarizations. |
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Re-evaluating Evaluation in Text Summarization (2020.emnlp-main)
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| Challenge: | Automated evaluation metrics are an essential part of the development of text-generation tasks such as summarization. |
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