The rapid expansion of text data has increased the need for effective methods to distill meaningful information from large datasets. Traditional and state-of-the-art approaches have made significant strides in topic modeling, yet they fall short in generating contextually specific and semantically intuitive topics, particularly in dynamic environments and low-resource languages. Additionally, multi-document summarization systems often struggle with issues like redundancy, scalability, and maintaining readability. We introduce FIDELITY (Fine-grained Interpretable Distillation for Effective Language Insights and Topic Yielding), a hybrid method that combines topic modeling and text summarization to produce fine-grained, semantically rich, and contextually relevant output. FIDELITY enhances dataset accessibility and interpretability, outperforming traditional models in topic diversity, similarity, and in the ability to process new, unseen documents. Additionally, it demonstrates robust multilingual capabilities, effectively handling low-resource languages like Tagalog. This makes FIDELITY a powerful tool for distilling and understanding complex textual data, providing detailed insights while maintaining the necessary granularity for practical applications.

Query-focused tabular summarization is an emerging task in table-to-text generation that synthesizes a summary response from tabular data based on user queries. Traditional transformer-based approaches face challenges due to token limitations and the complexity of reasoning over large tables. To address these challenges, we introduce DETQUS (Decomposition-Enhanced Transformers for QUery-focused Summarization), a system designed to improve summarization accuracy by leveraging tabular decomposition alongside a fine-tuned encoder-decoder model. DETQUS employs a large language model to selectively reduce table size, retaining only query-relevant columns while preserving essential information. This strategy enables more efficient processing of large tables and enhances summary quality. Our approach, equipped with table-based QA model Omnitab, achieves a ROUGE-L score of 0.4437, outperforming the previous state-ofthe- art REFACTOR model (ROUGE-L: 0.422). These results highlight DETQUS as a scalable and effective solution for query-focused tabular summarization, offering a structured alternative to more complex architectures.

This paper presents our submission to the AmericasNLP 2024 Shared Task on the Creation of Educational Materials for Indigenous Languages. We frame this task as one of morphological inflection generation, treating each sentence as a single word. We investigate and compare two distinct approaches: fine-tuning neural encoder-decoder models such as NLLB- 200, and in-context learning with proprietary large language models (LLMs). Our findings demonstrate that for this task, no one approach is perfect. Anthropic’s Claude 3 Opus, when supplied with grammatical description entries, achieves the highest performance on Bribri among the evaluated models. This outcome corroborates and extends previous research exploring the efficacy of in-context learning in low- resource settings. For Maya, fine-tuning NLLB- 200-3.3B using StemCorrupt augmented data yielded the best performance.