This paper examines how well visual language models (VLMs) understand video question answering (VideoQA) tasks and generate responses accordingly. Recently, VLMs based on Large Language Models (LLMs) have shown remarkable performance, but the processes of understanding and reasoning in VLMs remain under-explored. To tackle this challenge, we propose Video Understanding and Response Consistency Assessment, VURCA, a framework that incorporates a fine-grained question generation and answering process to measure how well the responses generated by VLMs align with what the model understands. In addition, we introduce an extended benchmark dataset, FgNExT-QA, which builds upon NExT-QA by incorporating more fine-grained VideoQA tasks. FgNExT-QA is designed to evaluate fine-grained understanding in video question answering. Through experiments, we found that despite the strong overall QA performance of VLMs, their understanding of both the video content and the question remains limited. In particular, they exhibit poor video comprehension in fine-grained VideoQA tasks.

Multi-stage long document summarization, which splits a long document as multiple segments and each of which is used to generate a coarse summary in multiple stage, and then the final summary is produced using the last coarse summary, is a flexible approach to capture salient information from the long document. Even if the coarse summary affects the final summary, however, the coarse summarizer in the existing multi-stage summarization is coarsely trained using data segments that are not useful to generate the final summary. In this paper, we propose a novel method for multi-stage long document summarization. The proposed method first generates new segment pairs, ensuring that all of them are relevant to generating the final summary. We then incorporate contrastive learning into the training of the coarse summarizer, which tries to maximize the similarities between source segments and the target summary during training. Through extensive experiments on six long document summarization datasets, we demonstrate that our proposed method not only enhances the existing multi-stage long document summarization approach, but also achieves performance comparable to state-of-the-art methods, including those utilizing large language models for long document summarization.