Nowadays, closed-source software only with stripped binaries still dominates the ecosystem, which brings obstacles to understanding the functionalities of the software and further conducting the security analysis. With such an urgent need, research has traditionally focused on predicting function names, which can only provide fragmented and abbreviated information about functionality. To advance the state-of-the-art, this paper presents Bin2Summary to automatically summarize the functionality of the function in stripped binaries with natural language sentences. Specifically, the proposed framework includes a functionality-specific code embedding module to facilitate fine-grained similarity detection and an attention-based seq2seq model to generate summaries in natural language. Based on 16 widely-used projects (e.g., Coreutils), we have evaluated Bin2Summary with 38,167 functions, which are filtered from 162,406 functions, and all of them have a high-quality comment. Bin2Summary achieves 0.728 in precision and 0.729 in recall on our datasets, and the functionality-specific embedding module can improve the existing assembly language model by up to 109.5% and 109.9% in precision and recall. Meanwhile, the experiments demonstrated that Bin2Summary has outstanding transferability in analyzing the cross-architecture (i.e., in x64 and x86) and cross-environment (i.e., in Cygwin and MSYS2) binaries. Finally, the case study illustrates how Bin2Summary outperforms the existing works in providing functionality summaries with abundant semantics beyond function names.