Question Generation (QG) is central to information retrieval, education, and knowledge assessment, yet its progress is bottlenecked by unreliable and non-scalable evaluation practices. Traditional metrics fall short in structured settings like document-grounded QG, and human evaluation, while insightful, remains expensive, inconsistent, and difficult to replicate at scale. We introduce AURA-QG: an Automated, Unsupervised, Replicable Assessment pipeline that scores question sets using only the source document. It captures four orthogonal dimensions i.e., answerability, non-redundancy, coverage, and structural entropy, without needing reference questions or relative baselines. Our method is modular, efficient, and agnostic to the question generation strategy. Through extensive experiments across four domains i.e., car manuals, economic surveys, health brochures, and fiction, we demonstrate its robustness across input granularities and prompting paradigms. Chain-of-Thought prompting, which first extracts answer spans and then generates targeted questions, consistently yields higher answerability and coverage, validating the pipeline’s fidelity. The metrics also exhibit strong agreement with human judgments, reinforcing their reliability for practical adoption. The complete implementation of our evaluation pipeline is publicly available.

Query-focused Summarization (QfS) deals with systems that generate summaries from document(s) based on a query. Motivated by the insight that Reinforcement Learning (RL) provides a generalization to Supervised Learning (SL) for Natural Language Generation, and thereby performs better (empirically) than SL, we use an RL-based approach for this task of QfS. Additionally, we also resolve the conflict of employing RL in Transformers with Teacher Forcing. We develop multiple Policy Gradient networks, trained on various reward signals: ROUGE, BLEU, and Semantic Similarity, which lead to a 10-point improvement over the State-of-the-Art approach on the ROUGE-L metric for a benchmark dataset (ELI5). We also show performance of our approach in zero-shot setting for another benchmark dataset (DebatePedia) – our approach leads to results comparable to baselines, which were specifically trained on DebatePedia. To aid the RL training, we propose a better semantic similarity reward, enabled by a novel Passage Embedding scheme developed using Cluster Hypothesis. Lastly, we contribute a gold-standard test dataset to further research in QfS and Long-form Question Answering (LfQA).

Most of the existing state of the art sentiment classification techniques involve the use of pre-trained embeddings. This paper postulates a generalized representation that collates training on multiple datasets using a Multi-task learning framework. We incorporate publicly available, pre-trained embeddings with Bidirectional LSTM’s to develop the multi-task model. We validate the representations on an independent test Irony dataset that can contain several sentiments within each sample, with an arbitrary distribution. Our experiments show a significant improvement in results as compared to the available baselines for individual datasets on which independent models are trained. Results also suggest superior performance of the representations generated over Irony dataset.