Papers by Jessica Ouyang
Contextualizing Generated Citation Texts (2024.lrec-main)
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| Challenge: | Abstractive citation text generation is usually framed as an infilling task . however, examining a recent LED-based citation generation system, we find that many of the generated citations are generic summaries of the reference paper’s main contribution, ignoring the citation context’s focus on a different topic. |
| Approach: | They propose a modification to the citation text generation task by training the generation model to generate a citation given a reference paper and the context window around the target. |
| Outcome: | The proposed model can generate citations based on the entire context window, including the target citation. |
A Robust Abstractive System for Cross-Lingual Summarization (N19-1)
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| Challenge: | We present a novel system for cross-lingual summarization that can be applied to low-resource languages. |
| Approach: | They propose a neural abstractive summarization system that can be applied to low-resource languages . they use machine translation and the New York Times summarizing corpus to create a corpus . |
| Outcome: | The proposed system achieves higher fluency than standard summarizers on translated documents . the proposed system can be easily applied to new low-resource languages . |
Measuring Cross-Text Cohesion for Segmentation Similarity Scoring (2024.lrec-main)
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| Challenge: | a new segmentation similarity metric is being developed for text segmentation . current metrics are content-agnostic and do not provide fine-grained scoring . |
| Approach: | They propose a word-embedding-based metric of cross-textual cohesion based on the formal linguistic definition of cohetion and incorporate it into a new segmentation similarity metric, SED. |
| Outcome: | The proposed metric provides fine-grained segmentation similarity scoring for 3 basic errors, avoiding limitations of traditional metrics. |
Neural Network Alignment for Sentential Paraphrases (P19-1)
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| Challenge: | a monolingual alignment system is ill-suited for word- or short phrase-based alignments. |
| Approach: | They propose a monolingual alignment system for long, sentence- or clause-level alignments . they show that systems designed for word- or short phrase-based alignment are ill-suited for longer alignments. |
| Outcome: | The proposed system outperforms state-of-the-art systems on long alignments . it achieves significantly higher recall on aligning phrases of four or more words . |
A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation (2024.lrec-main)
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| Challenge: | Knowledge-based open-domain dialogue generation aims to build chit-chat systems that talk to humans using mined support knowledge. |
| Approach: | They propose a benchmark for evaluating multi-source dialogue knowledge selection and response generation using Wikipedia's wizard of Wikipedia. |
| Outcome: | The proposed benchmark is called multi-source Wizard of Wikipedia (Ms.WoW) it contains clean support knowledge, grounded at the utterance level and partitioned into multiple knowledge sources. |
How Does Knowledge Selection Help Retrieval Augmented Generation? (2025.findings-emnlp)
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| Challenge: | Retrieval-augmented generation (RAG) is a powerful method for enhancing natural language generation by integrating external knowledge into a model’s output. |
| Approach: | They empirically analyze how knowledge selection influences downstream generation performance in RAG systems by simulating different retrieval and selection conditions through a controlled mixture of gold and distractor knowledge. |
| Outcome: | The proposed model is based on a controlled mixture of gold and distractor knowledge and simulated with a gold and distractors. |
Related Work and Citation Text Generation: A Survey (2024.emnlp-main)
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| Challenge: | Academic research paper authors must perform literature review to compare work with prior work . authors must compose coherent story that connects prior work and current work based on author's understanding of field . |
| Approach: | They propose to use automatic related work generation (RWG) to generate papers . authors summarize key approaches and define tasks in a zoo of historical works . |
| Outcome: | a new study summarises key approaches and defines the tasks and discusses the challenges of RWG. |
CORWA: A Citation-Oriented Related Work Annotation Dataset (2022.naacl-main)
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| Challenge: | Academic research is an exploratory activity to discover new solutions to problems . prior work focused on the sentence as the basic unit of generation, neglecting that related work sections consist of variable length text fragments derived from different information sources. |
| Approach: | They propose a Citation Oriented Related Work Annotation dataset that labels citation text fragments . they propose linguistically-motivated framework for human-in-the-loop, abstractive related work generation . |
| Outcome: | The proposed framework is based on a Citation Oriented Related Work Annotation dataset . it automatically tags unlabeled related work sections on the dataset based upon the proposed model . |
Explaining Relationships Among Research Papers (2025.coling-main)
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| Challenge: | Existing literature reviews focus on summarizing individual papers without addressing the need for expository and transition sentences to explain the relationships among multiple papers. |
| Approach: | They propose a feature-based, LLM-prompting approach to generate richer citation texts . they propose to use related work sections of scientific articles as proxy for the kind of short, customized, daily feed summaries . |
| Outcome: | The proposed approach captures complex relationships among multiple papers while generating richer citation texts. |
GRASP: Graph-Reasoning Aided Survey Planning for High-Fidelity Related Work Generation (2026.findings-acl)
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| Challenge: | Graph-Reasoning Aided Survey Planning (GRASP) is a framework for analyzing cited papers. |
| Approach: | They propose a framework that combines LLM planning for related work generation with graph algorithms to extract key relationships among cited papers. |
| Outcome: | The proposed framework generates RWS that closely match human-written targets in terms of discourse roles, intents, and grouping of citations. |