Papers by Nianlong Gu
Embedding-based Scientific Literature Discovery in a Text Editor Application (2020.acl-demos)
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| Challenge: | Despite the availability of powerful search engines and text editing software, discovering relevant papers and integrating the knowledge into a manuscript remain complex tasks associated with high cognitive load. |
| Approach: | They propose to combine text editing and literature discovery in an interactive user interface with a search engine that couples Boolean keyword filtering with nearest neighbor search over text embeddings. |
| Outcome: | The proposed application combines text editing and literature discovery in an interactive user interface. |
SpiritRAG: A Q&A System for Religion and Spirituality in the United Nations Archive (2025.emnlp-demos)
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Yingqiang Gao, Fabian Winiger, Patrick Montjourides, Anastassia Shaitarova, Nianlong Gu, Simon Peng-Keller, Gerold Schneider
| Challenge: | Religion and spirituality (R/S) are complex and domain-dependent concepts that have long confounded researchers and policymakers. |
| Approach: | They propose an interactive question-answering system based on Retrieval-Augmented Generation (RAG) SpiritRAG allows researchers and policymakers to conduct complex, context-sensitive database searches of large datasets . |
| Outcome: | SpiritRAG is an interactive Q&A system based on Retrieval-Augmented Generation (RAG) built using 7,500 UN resolution documents related to religion and spirituality in the domains of health and education. |
GreedyCAS: Unsupervised Scientific Abstract Segmentation with Normalized Mutual Information (2023.emnlp-main)
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| Challenge: | Abstracts of scientific papers typically contain premises and conclusions, but in non-structured abstracts the concluding information is not marked. |
| Approach: | They propose to use Normalized Mutual Information (NMI) to optimize the NMI score between two segments by assuming that conclusions are strongly semantically linked with preceding premises. |
| Outcome: | The proposed approach outperforms baseline methods on structured abstracts and on non-structured abstracts. |
Evaluating Unsupervised Argument Aligners via Generation of Conclusions of Structured Scientific Abstracts (2024.eacl-short)
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| Challenge: | Scientific abstracts provide a concise summary of research findings. |
| Approach: | They evaluate unsupervised approaches for extracting scientific arguments as aligned premise-conclusion pairs . they find mutual information outperforms other measures on this task . |
| Outcome: | The proposed methods outperform language models on the task of extracting scientific arguments from abstracts. |
SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation (2023.acl-demo)
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| Challenge: | Scientific writing involves retrieving, summarizing, and citing relevant papers. |
| Approach: | They propose a pipeline that automatically recommends relevant papers, extracts highlights, and suggests a reference sentence as a citation of a paper. |
| Outcome: | The proposed pipeline recommends relevant papers from large databases of hundreds of millions of papers . it provides extractive summaries and abstractively-generated citation sentences . authors question whether it is possible to partly automate this process to reduce cognitive load . |
MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes (2022.acl-long)
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| Challenge: | MemSum is a reinforcement-learning-based extractive summarizer that considers the text content of the sentence, the global context of the rest of the document, and the extraction history of the sentences that have already been extracted. |
| Approach: | They propose a reinforcement-learning-based extractive summarizer that iteratively selects sentences from a broad set of information that would intuitively be used by humans. |
| Outcome: | The proposed extractive summarizer is enriched with information on the extraction history and local, global, and historical information. |