Large Language Models (LLMs) have demonstrated superior performance in listwise passage reranking task. However, directly applying them to rank long-form documents introduces both effectiveness and efficiency issues due to the substantially increased context length. To address this challenge, we propose a per-document summarization model SumRank, aligned with downstream listwise reranking, to compress long-form documents into concise rank-aligned summaries before the final listwise reranking stage. To develop our summarization model SumRank, we introduce a three-stage training pipeline consisting of cold-start Supervised Fine-Tuning (SFT), specialized Reinforcement Learning (RL) data construction, and rank-driven alignment via RL. This paradigm aligns the SumRank with downstream ranking objectives to preserve crucial relevance signals. We conduct extensive experiments on both in-domain (ID) benchmark datasets from the TREC Deep Learning tracks and out-of-domain (OOD) datasets from the BEIR benchmark. Results show that our lightweight SumRank model achieves state-of-the-art (SOTA) ranking performance while significantly improving efficiency by reducing both summarization overhead and reranking complexity.