Information extraction (IE) in specialized domains like computer science and chemistry is challenged by the poor generalization of traditional models and the knowledge deficits of general-purpose Large Language Models (LLMs). We introduce a robust, LLM-based framework featuring two core contributions: an end-to-end training and inference paradigm that combines continual pre-training (CPT) for knowledge injection, supervised fine-tuning (SFT) for task alignment, and retrieval-augmented generation (RAG) for inference-time enhancement; and a novel LLM-assisted data annotation pipeline for the efficient creation of high-quality training data. Comprehensive experiments demonstrate that while fine-tuning alone yields strong in-domain performance, our complete framework exhibits superior robustness and generalization. It consistently achieves state-of-the-art results in challenging domain-shift and novel-schema scenarios, validating our integrated approach for building adaptable and high-performance domain-specific IE systems.

This paper summarizes two approaches developed for BioNLP2023 workshop task 1A: clinical problem list summarization. We develop two types of methods with either rules or pre-trained language models. In the rule-based summarization model, we leverage UMLS (Unified Medical Language System) and a negation detector to extract text spans to represent the summary. We also fine tune three pre-trained language models (BART, T5 and GPT2) to generate the summaries. Experiment results show the rule based system returns extractive summaries but lower ROUGE-L score (0.043), while the fine tuned T5 returns a higher ROUGE-L score (0.208).

Abstractive summarization has made tremendous progress in recent years. In this work, we perform fine-grained human annotations to evaluate long document abstractive summarization systems (i.e., models and metrics) with the aim of implementing them to generate reliable summaries. For long document abstractive models, we show that the constant strive for state-of-the-art ROUGE results can lead us to generate more relevant summaries but not factual ones. For long document evaluation metrics, human evaluation results show that ROUGE remains the best at evaluating the relevancy of a summary. It also reveals important limitations of factuality metrics in detecting different types of factual errors and the reasons behind the effectiveness of BARTScore. We then suggest promising directions in the endeavor of developing factual consistency metrics. Finally, we release our annotated long document dataset with the hope that it can contribute to the development of metrics across a broader range of summarization settings.