Generating Formality-Tuned Summaries Using Input-Dependent Rewards
Abstractive text summarization aims at generating human-like summaries by understanding and paraphrasing the given input content. Recent efforts based on sequence-to-sequence networks only allow the generation of a single summary. However, it is often desirable to accommodate the psycho-linguistic preferences of the intended audience while generating the summaries. In this work, we present a reinforcement learning based approach to generate formality-tailored summaries for an input article. Our novel input-dependent reward function aids in training the model with stylistic feedback on sampled and ground-truth summaries together. Once trained, the same model can generate formal and informal summary variants. Our automated and qualitative evaluations show the viability of the proposed framework.
Code (0)
등록된 구현이 없습니다.
Tasks
Abstractive Text Summarizationreinforcement-learningReinforcement LearningReinforcement Learning (RL)Text SummarizationSimilar Papers 제목 키워드 기반
Multi-Task Neural Models for Translating Between Styles Within and Across Languages
Generating natural language requires conveying content in an appropriate style. We explore two related tasks on generating text of varying formality: monolingual formality transfer and formality-sensitive machine transla…
Machine TranslationMulti-Task LearningTranslationGenerating Topic-Oriented Summaries Using Neural Attention
Summarizing a document requires identifying the important parts of the document with an objective of providing a quick overview to a reader. However, a long article can span several topics and a single summary cannot do …
Abstractive Text SummarizationText SummarizationLearning a Formality-Aware Japanese Sentence Representation
While the way intermediate representations are generated in encoder-decoder sequence-to-sequence models typically allow them to preserve the semantics of the input sentence, input features such as formality might be left…
DecoderSentenceFine-tuning Large Language Models for Automated Diagnostic Screening Summaries
Improving mental health support in developing countries is a pressing need. One potential solution is the development of scalable, automated systems to conduct diagnostic screenings, which could help alleviate the burden…
DiagnosticA Comparative Study of Recent Large Language Models on Generating Hospital Discharge Summaries for Lung Cancer Patients
Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language m…
Semantic SimilaritySemantic Textual Similarity