Papers by Zhihong Shen
Word Graph Guided Summarization for Radiology Findings (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies focus on introducing salient word information to general text summarization framework to guide selection of key content in radiology findings. |
| Approach: | They propose a method for automatic impression generation using word graphs and a Word Graph guided Summarization model to capture critical words and their relations. |
| Outcome: | The proposed method is validated on two datasets, OPENI and MIMIC-CXR. |
Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained language models (LMs) have shown effectiveness in literature understanding tasks, especially when tuned via contrastive learning. |
| Approach: | They propose a multi-task contrastive learning framework that enables common knowledge sharing across different scientific literature understanding tasks while preventing task-specific skills from interfering with each other. |
| Outcome: | The proposed framework outperforms state-of-the-art pre-trained language models on a comprehensive dataset. |
Cross-modal Memory Networks for Radiology Report Generation (2021.acl-long)
Copied to clipboard
| Challenge: | Medical imaging reports are essential in clinical practice, and generating the reports is beneficial to reduce the burden of radiologists. |
| Approach: | They propose to use a shared memory to enhance the encoder-decoder framework for radiology report generation. |
| Outcome: | The proposed model can generate more accurate reports on two widely used datasets. |
DGLF: A Dual Graph-based Learning Framework for Multi-modal Sarcasm Detection (2024.emnlp-main)
Copied to clipboard
Zhihong Zhu, Kefan Shen, Zhaorun Chen, Yunyan Zhang, Yuyan Chen, Xiaoqi Jiao, Zhongwei Wan, Shaorong Xie, Wei Liu, Xian Wu, Yefeng Zheng
| Challenge: | Existing methods for multimodal sarcasm detection neglect high-order relationships and underestimate high-frequency messages. |
| Approach: | They propose a Dual Graph-based Learning Framework to capture inter-modal inconsistencies . they propose combining a hypergraph and a vanilla graph to achieve enhanced propagation . |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on two benchmark datasets. |
Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work has proposed to improve relevance modeling by having large language models actively involved in retrieval, i.e., to guide retrieval with generation. |
| Approach: | They propose to have large language models actively involved in retrieval to guide retrieval with generation. |
| Outcome: | The proposed method synergizes retrieval and generation in an iterative manner, and can generate better results in subsequent iterations. |
Explainable and Sparse Representations of Academic Articles for Knowledge Exploration (2020.coling-main)
Copied to clipboard
| Challenge: | a system for summarizing academic articles by concept tagging has shown great coverage and high accuracy of concept identification. |
| Approach: | They propose to transform tagged concepts into sparse vectors as representations of academic documents. |
| Outcome: | The proposed system can be applied to a broader class of applications. |
Reasoning Fails Where Step Flow Breaks (2026.acl-long)
Copied to clipboard
| Challenge: | Existing analysis tools struggle with long chain of thought traces. |
| Approach: | They propose a saliency-inspired test-time intervention that adjusts shallow saliencies to improve accuracy on math, science, and coding tasks. |
| Outcome: | The proposed model improves accuracy on math, science, and coding tasks without retraining. |
SciConceptMiner: A system for large-scale scientific concept discovery (2021.acl-demo)
Copied to clipboard
| Challenge: | SciConceptMiner is a self-supervised system for the capture of scientific concepts . the system is scalable to the size of documents and the number of topics it can model . |
| Approach: | They propose a self-supervised system for the automatic capture of scientific concepts from academic publications and semi-structured data. |
| Outcome: | The proposed system achieves high accuracy (94.7%) with more than 740K scientific concepts. |
A Web-scale system for scientific knowledge exploration (P18-4)
Copied to clipboard
| Challenge: | a system that organizes scientific knowledge into a hierarchical concept structure is needed to enable efficient exploration of Web-scale knowledge. |
| Approach: | They propose a system that organizes scientific knowledge into a hierarchical concept structure . system allows researchers to identify hundreds of thousands of scientific concepts . it also allows researchers tagging scientific publications into millions of concepts based on text and graph structure based model . |
| Outcome: | The proposed system builds the most comprehensive cross-domain scientific concept ontology published to date, with more than 200 thousand concepts and over one million relationships. |