TopicMerge is a multi-stage pipeline that combines efficient embedding-based clustering with Large Language Model (LLM) guidance to produce high-quality topic models. The method first over-segments a corpus into fine-grained micro-clusters, then iteratively merges them using an LLM that evaluates candidate pairs based on local context. Experiments on Bills and Wiki datasets demonstrate state‑of‑the‑art clustering performance while cutting LLM calls by more than 92% compared to document‑level approaches.
By Matthew Raffel, Lizhong Chen, Sai Munikoti, Sameera Horawalavithana
arXiv:2606. 28328v1 Announce Type: cross Abstract: In recent years, text clustering has become a critical technique for applications including intent discovery, topic mining, and recommendation systems.
By Daoming Wan, Yizheng Huang, Jimmy X. Huang
TopiCLEAR is a framework that clusters document or sentence embeddings using adaptive dimensionality reduction to uncover low‑dimensional geometric structures that correspond to human‑interpretable topics. The method is evaluated on four benchmark datasets, showing strong agreement with human annotations, especially for short and informal texts. A Twitter case study demonstrates that TopiCLEAR yields more interpretable topics than LDA, recovering both annotated topic structure and coherent sub‑topics.
By Aoi Fujita, Taichi Yamamoto, Yuri Nakayama, Ryota Kobayashi
ClusterFusion is a hybrid text‑clustering framework that uses a large language model (LLM) as the core clustering engine, guided by lightweight embedding methods. The process involves embedding‑guided subset partitioning, LLM‑driven topic summarization, and LLM‑based topic assignment, allowing direct incorporation of domain knowledge and user preferences. Experiments on three public benchmarks and two new domain‑specific datasets show that ClusterFusion achieves state‑of‑the‑art performance on standard tasks and significant gains in specialized domains, with the authors releasing a new dataset and benchmark results.
By Yiming Xu, Yuan Yuan, Vijay Viswanathan, Graham Neubig
SentenceETM is a neural topic model that assigns a topic to each sentence by jointly using lexical and contextual evidence. It represents each topic with a vocabulary distribution via word embeddings and a Gaussian over sentence embeddings in a topic‑specific low‑dimensional subspace. The model exactly marginalizes the sentence topic, combining word and embedding evidence, and uses amortized variational inference for document proportions while re‑estimating Gaussian parameters in closed form.
By Federica Granese, Serena Villata, Charles Bouveyron
MonoTM is an interpretable topic modeling framework that separates the estimation of document–topic mixtures from the generation of topic descriptors. It uses sparse autoencoders to extract dense, interpretable features for mixture estimation, then learns topic descriptors from a distinct set of corpus‑grounded semantic features. This approach preserves global topic structure while providing more meaningful, semantic‑unit descriptors than traditional top‑word lists.
By Una Joh, Bei Yu
arXiv:2510.15125v3 Announce Type: replace-cross
Abstract: Social media platforms play a pivotal role in shaping political discourse, but the scale and rapid evolution of online content make systemati...
By Alexander Brady, Tunazzina Islam
BERTilda is an explainable framework for tracking topic lifecycles in longitudinal text streams. It discovers topics independently in each time window using an embedding‑based topic model, then links topics across adjacent windows via a temporal graph that uses both semantic similarity and a bidirectional coverage signal derived from tweet‑to‑topic attribution. The graph‑based rules identify continuations, splits, merges, disappearances, and unclear transitions, and the method achieves up to 87% agreement with human annotators on a gold‑standard subset.
By Cl\'audia Oliveira, \'Alvaro Figueira
The paper introduces SeLATM, a framework that improves topic modeling by generating topics at the segment level and refining them through agentic feedback loops. This approach addresses limitations of LLM-based topic assignment methods, such as the inability to produce topic distributions, overly broad or narrow topics, and high resource consumption. Experiments on multiple datasets show that SeLATM reduces LLM resource usage while maintaining superior performance.
By Myeongjun Erik Jang, Antonios Georgiadis, Sae Young Moon, Fran Silavong
arXiv:2602. 17907v2 Announce Type: replace-cross Abstract: Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking contextual information and struggling with data sparsity.
By Raymond Li, Amirhossein Abaskohi, Chuyuan Li, Gabriel Murray, Giuseppe Carenini
arXiv:2602. 17907v3 Announce Type: replace-cross Abstract: Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking contextual information and struggling with data sparsity.
By Raymond Li, Amirhossein Abaskohi, Chuyuan Li, Gabriel Murray, Giuseppe Carenini
ConvergeWriter introduces a bottom‑up, data‑driven framework for long‑form document generation that first retrieves exhaustive knowledge from a source corpus and clusters it into distinct knowledge groups. These clusters then guide the creation of a hierarchical outline and the final text, ensuring the output is strictly grounded in the retrieved material and traceable to its sources. Experiments on 14B and 32B LLMs show that this approach matches or surpasses state‑of‑the‑art baselines, especially in scenarios requiring high factual fidelity and structural coherence.
By Binquan Ji, Jiaqi Wang, Ruiting Li, Xingchen Han, Yiyang Qi, Shichao Wang, Yifei Lu, Yuantao Han, Feiliang Ren