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Found 9 papers, 7 papers with code
Length Control in Abstractive Summarization by Pretraining Information Selection
In this paper, we propose a length-aware attention mechanism (LAAM) to adapt the encoding of the source based on the desired length.
Reference-free Summarization Evaluation via Semantic Correlation and Compression Ratio
In this paper, we propose a new automatic reference-free evaluation metric that compares semantic distribution between source document and summary by pretrained language models and considers summary compression ratio.
Improving Topic Relevance Model by Mix-structured Summarization and LLM-based Data Augmentation
To tackle above two problems, we first take query concatenated with the query-based summary and the document summary without query as the input of topic relevance model, which can help model learn the relevance degree between query and the core topic of document.
Zero-shot Faithfulness Evaluation for Text Summarization with Foundation Language Model
Despite tremendous improvements in natural language generation, summarization models still suffer from the unfaithfulness issue.
In-sample Curriculum Learning by Sequence Completion for Natural Language Generation
Curriculum learning has shown promising improvements in multiple domains by training machine learning models from easy samples to hard ones.
Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions
Abstractive dialogue summarization is to generate a concise and fluent summary covering the salient information in a dialogue among two or more interlocutors.
Post-Training Dialogue Summarization using Pseudo-Paraphrasing
Previous dialogue summarization techniques adapt large language models pretrained on the narrative text by injecting dialogue-specific features into the models.
Multi-turn Response Selection using Dialogue Dependency Relations
In this paper, we propose a dialogue extraction algorithm to transform a dialogue history into threads based on their dependency relations.
Controlling Length in Abstractive Summarization Using a Convolutional Neural Network
Convolutional neural networks (CNNs) have met great success in abstractive summarization, but they cannot effectively generate summaries of desired lengths.
