VKIE: The Application of Key Information Extraction on Video Text

Extracting structured information from videos is critical for numerous downstream applications in the industry. In this paper, we define a significant task of extracting hierarchical key information from visual texts on videos. To fulfill this task, we decouples it into four subtasks and introduce two implementation solutions called PipVKIE and UniVKIE. PipVKIE sequentially completes the four subtasks in continuous stages, while UniVKIE is improved by unifying all the subtasks into one backbone. Both PipVKIE and UniVKIE leverage multimodal information from vision, text, and coordinates for feature representation. Extensive experiments on one well-defined dataset demonstrate that our solutions can achieve remarkable performance and efficient inference speed. The code and dataset will be publicly available

Unsupervised Extractive Summarization with Heterogeneous Graph Embeddings for Chinese Document

In the scenario of unsupervised extractive summarization, learning high-quality sentence representations is essential to select salient sentences from the input document. Previous studies focus more on employing statistical approaches or pre-trained language models (PLMs) to extract sentence embeddings, while ignoring the rich information inherent in the heterogeneous types of interaction between words and sentences. In this paper, we are the first to propose an unsupervised extractive summarizaiton method with heterogeneous graph embeddings (HGEs) for Chinese document. A heterogeneous text graph is constructed to capture different granularities of interactions by incorporating graph structural information. Moreover, our proposed graph is general and flexible where additional nodes such as keywords can be easily integrated. Experimental results demonstrate that our method consistently outperforms the strong baseline in three summarization datasets

MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation

We propose MemoChat, a pipeline for refining instructions that enables large language models (LLMs) to effectively employ self-composed memos for maintaining consistent long-range open-domain conversations. We demonstrate a long-range open-domain conversation through iterative "memorization-retrieval-response" cycles. This requires us to carefully design tailored tuning instructions for each distinct stage. The instructions are reconstructed from a collection of public datasets to teach the LLMs to memorize and retrieve past dialogues with structured memos, leading to enhanced consistency when participating in future conversations. We invite experts to manually annotate a test set designed to evaluate the consistency of long-range conversations questions. Experiments on three testing scenarios involving both open-source and API-accessible chatbots at scale verify the efficacy of MemoChat, which outperforms strong baselines.Comment: Codes, data and models will be available soo

Inhibition the ubiquitination of ENaC and Na,K-ATPase with erythropoietin promotes alveolar fluid clearance in sepsis-induced acute respiratory distress syndrome

Sepsis-induced acute respiratory distress syndrome (ARDS) causes significant fatalities worldwide and lacks pharmacological intervention. Alveolar fluid clearance (AFC) plays a pivotal role in the remission of ARDS and is markedly impaired in the pathogenesis of ARDS. Here, we demonstrated that erythropoietin could effectively ameliorate lung injury manifestations and lethality, restore lung function and promote AFC in a rat model of lipopolysaccharide (LPS)-induced ARDS. Moreover, it was proven that EPO-induced restoration of AFC occurs through triggering the total protein expression of ENaC and Na,K-ATPase channels, enhancing their protein abundance in the membrane, and suppressing their ubiquitination for degeneration. Mechanistically, the data indicated the possible involvement of EPOR/JAK2/STAT3/SGK1/Nedd4–2 signaling in this process, and the pharmacological inhibition of the pathway markedly eliminated the stimulating effects of EPO on ENaC and Na,K-ATPase, and subsequently reversed the augmentation of AFC by EPO. Consistently, in vitro studies of alveolar epithelial cells paralleled with that EPO upregulated the expression of ENaC and Na,K-ATPase, and patch-clamp studies further demonstrated that EPO substantially strengthened sodium ion currents. Collectively, EPO could effectively promote AFC by improving ENaC and Na,K-ATPase protein expression and abundance in the membrane, dependent on inhibition of ENaC and Na,K-ATPase ubiquitination, and resulting in diminishing LPS-associated lung injuries