mGDTP: An End-to-End Framework for Long Document Modeling with mix-Granularity Dynamic Token Pruning
Authors: beiya dai, Meilin Chen, Yi Xu, Peng Yu, Zhouhan Lin, Xinbing Wang, Chenghu Zhou


Reviewer paper summaries: This paper presents mGDTP, an end-to-end pruning approach to modeling long documents in the ranking scenario. Experimental results on three benchmarks show that the proposed method demonstrates superior effectiveness than the previous end-to-end methods and still maintain an acceptable efficiency.
Summary of strengths: 1. This paper aims to optimize the effectiveness of ranking long document while maintain an acceptable efficiency and the topic is important and meaningful.
2. The paper conducts extensive experiments on multiple benchmarks.
3. The experimental results show that the proposed methods outperform other end-to-end methods.
Summary of weaknesses: 1. The writing in the method part should be polished to improve clarity and readability, such as the usage and explanations of symbols and formulas.
2. Lack of comparison with some recent end-to-end ranking models that can take long inputs, such as bge-m3 and jina-reranker. 
3. The proposed method lacks interpretability compared to existing methods, and more comprehensive experiments are needed.
Comments, suggestions and typos: 1. It would be nice to add the baselines mentioned above and analyze their effectiveness and efficiency.
2. The roles of DIM and Multi-level TM modules are unclear. As mentioned in Sec 4.4.2, when w/o Multi-Level TM, “the model selects a substantial number of tokens irrelevant to the query” (line 508). I would suggest applying this analysis for mGDTP and w/o DIM and present the results in the paper.
3. As I understand, this pruning method will destroy the original structure of the document and  I'm curious about what the input looks like after pruning. If the analysis mentioned in comment 2 as well as a case study could be added, it would be clearer.
Overall assessment scores: 2.5