用排序学习提升摘要相关性，让回答更准更连贯。
Learning to Rank Salient Content for Query-focused Summarization
- 用共享解码器在段落级做内容排序，优化摘要优先级
- QMSum上Rouge-L提升0.42，BertScore提升0.34，效果显著
- 适合处理宽泛查询，尤其擅长生成高相关、忠实的摘要
本研究探索将学习排序（LTR）与查询聚焦摘要（QFS）结合，通过段落级内容优先级排序提升摘要相关性。采用与摘要解码器共享的二级解码器执行LTR任务。相比现有最优模型，本方法在QMSum基准上所有指标均表现更优，在SQuALITY基准上两项指标持平，同时训练开销更低。具体而言，在QMSum上，模型在Rouge-L上提升0.42，BertScore提升0.34，表明理解与相关性增强；在SQuALITY上，尽管Rouge-1和Rouge-2略有不足，但Rouge-L提升1.47，显示其生成连贯摘要的能力。人工评估证实该方法在相关性和忠实度上有效，且不影响流畅性。定性分析表明，模型对宽泛查询优于特定查询。此外，本文进行了错误分析，指出了当前挑战并提出未来研究方向。
原文摘要 · Abstract (English)
This study examines the potential of integrating Learning-to-Rank (LTR) with Query-focused Summarization (QFS) to enhance the summary relevance via content prioritization. Using a shared secondary decoder with the summarization decoder, we carry out the LTR task at the segment level. Compared to the state-of-the-art, our model outperforms on QMSum benchmark (all metrics) and matches on SQuALITY benchmark (2 metrics) as measured by Rouge and BertScore while offering a lower training overhead. Specifically, on the QMSum benchmark, our proposed system achieves improvements, particularly in Rouge-L (+0.42) and BertScore (+0.34), indicating enhanced understanding and relevance. While facing minor challenges in Rouge-1 and Rouge-2 scores on the SQuALITY benchmark, the model significantly excels in Rouge-L (+1.47), underscoring its capability to generate coherent summaries. Human evaluations emphasize the efficacy of our method in terms of relevance and faithfulness of the generated summaries, without sacrificing fluency. A deeper analysis reveals our model's superiority over the state-of-the-art for broad queries, as opposed to specific ones, from a qualitative standpoint. We further present an error analysis of our model, pinpointing challenges faced and suggesting potential directions for future research in this field.
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