Xiangci Li, Zhiyu Chen, Jason Ingyu Choi, Nikhita Vedula, Besnik Fetahu, Oleg Rokhlenko, Shervin Malmasi. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

The rapid pace of research publications makes it challenging for researchers to stay up to date. There is a growing need for automatically generated, concise literature reviews to help researchers quickly identify papers relevant to their interests. Prior work over the past decade has focused on summarizing individual research papers, typically in the context of citation generation, while the relationships among multiple papers have largely been overlooked. Existing approaches primarily generate standalone citation sentences without addressing the need for expository and transition sentences to explain the relationships among multiple citations. In this work, we propose a feature-based, LLM-prompting approach to generate richer citation texts and simultaneously capture the complex relationships among multiple papers. Our expert evaluation reveals a strong correlation between human preference and integrative writing styles, indicating that readers favor high-level, abstract citations with transition sentences that weave them into a coherent narrative.

Knowledge-based, open-domain dialogue generation aims to build chit-chat systems that talk to humans using mined support knowledge. Many types and sources of knowledge have previously been shown to be useful as support knowledge. Even in the era of large language models, response generation grounded in knowledge retrieved from additional up-to-date sources remains a practically important approach. While prior work using single-source knowledge has shown a clear positive correlation between the performances of knowledge selection and response generation, there are no existing multi-source datasets for evaluating support knowledge retrieval. Further, prior work has assumed that the knowledge sources available at test time are the same as during training. This unrealistic assumption unnecessarily handicaps models, as new knowledge sources can become available after a model is trained. In this paper, we present a high-quality benchmark named multi-source Wizard of Wikipedia (Ms.WoW) for evaluating multi-source dialogue knowledge selection and response generation. Unlike existing datasets, it contains clean support knowledge, grounded at the utterance level and partitioned into multiple knowledge sources. We further propose a new challenge, dialogue knowledge plug-and-play, which aims to test an already trained dialogue model on using new support knowledge from previously unseen sources in a zero-shot fashion.

Abstractive citation text generation is usually framed as an infilling task, where a sequence-to-sequence model is trained to generate a citation given a reference paper and the context window around the target; the generated citation should be a brief discussion of the reference paper as it relates to the citing context. 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. To address this problem, we propose a simple modification to the citation text generation task: the generation target is not only the citation itself, but the entire context window, including the target citation. This approach can be easily applied to any abstractive citation generation system, and our experimental results show that training in this way is preferred by human readers and allows the generation model to make use of contextual clues about what topic to discuss and what stance to take.

To convince readers of the novelty of their research paper, authors must perform a literature review and compose a coherent story that connects and relates prior works to the current work.This challenging nature of literature review writing makes automatic related work generation (RWG) academically and computationally interesting, and also makes it an excellent test bed for examining the capability of SOTA natural language processing (NLP) models.Since the initial proposal of the RWG task, its popularity has waxed and waned, following the capabilities of mainstream NLP approaches.In this work, we survey the zoo of RWG historical works, summarizing the key approaches and task definitions and discussing the ongoing challenges of RWG.

Academic research is an exploratory activity to discover new solutions to problems.By this nature, academic research works perform literature reviews to distinguish their novelties from prior work.In natural language processing, this literature review is usually conducted under the "Related Work" section.The task of related work generation aims to automatically generate the related work section given the rest of the research paper and a list of papers to cite.Prior work on this task has focused on the sentence as the basic unit of generation, neglecting the fact that related work sections consist of variable length text fragments derived from different information sources.As a first step toward a linguistically-motivated related work generation framework, we present a Citation Oriented Related Work Annotation (CORWA) dataset that labels different types of citation text fragments from different information sources.We train a strong baseline model that automatically tags the CORWA labels on massive unlabeled related work section texts.We further suggest a novel framework for human-in-the-loop, iterative, abstractive related work generation.

Evidence plays a crucial role in any biomedical research narrative, providing justification for some claims and refutation for others.We seek to build models of scientific argument using information extraction methods from fulltext papers.We present the capability of automatically extracting text fragments from primary research papers that describe the evidence presented in that paper's figures, which arguably provides the raw material of any scientific argument made within the paper.We apply richly contextualized deep representation learning pre-trained on biomedical domain corpus to the analysis of scientific discourse structures and the extraction of "evidence fragments" (i.e., the text in the results section describing data presented in a specified subfigure) from a set of biomedical experimental research articles.We first demonstrate our state-of-the-art scientific discourse tagger on two scientific discourse tagging datasets and its transferability to new datasets.We then show the benefit of leveraging scientific discourse tags for downstream tasks such as claim-extraction and evidence fragment detection.Our work demonstrates the potential of using evidence fragments derived from figure spans for improving the quality of scientific claims by cataloging, indexing and reusing evidence fragments as independent documents.