  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).

Computation and Language;Artificial Intelligence