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Found 19 papers, 2 papers with code
Towards Generating Query to Perform Query Focused Abstractive Summarization using Pre-trained Model
Then, we fine-tuned the BERTSUM model for generating the abstractive summaries.
Generating Query Focused Summaries without Fine-tuning the Transformer-based Pre-trained Models
Finally, we used the MMR approach again to select the query relevant sentences from the generated summaries of individual pre-trained models and constructed the final summary.
Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization
In Natural Language Processing, multi-document summarization (MDS) poses many challenges to researchers above those posed by single-document summarization (SDS).
Transformer and seq2seq model for Paraphrase Generation
For better quality of generated paraphrases, we propose a framework that combines the effectiveness of two models {--} transformer and sequence-to-sequence (seq2seq).
Transformer-based Model for Single Documents Neural Summarization
We propose a system that improves performance on single document summarization task using the CNN/DailyMail and Newsroom datasets.
Automatic Opinion Question Generation
We study the problem of opinion question generation from sentences with the help of community-based question answering systems.
Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion
Furthermore, we apply our sentence level model to implement an abstractive multi-document summarization system where documents usually contain a related set of sentences.
Towards Abstractive Multi-Document Summarization Using Submodular Function-Based Framework, Sentence Compression and Merging
We propose a submodular function-based summarization system which integrates three important measures namely importance, coverage, and non-redundancy to detect the important sentences for the summary.
Extract with Order for Coherent Multi-Document Summarization
In this work, we aim at developing an extractive summarizer in the multi-document setting.
Complex Question Answering: Unsupervised Learning Approaches and Experiments
Complex questions that require inferencing and synthesizing information from multiple documents can be seen as a kind of topic-oriented, informative multi-document summarization where the goal is to produce a single text as a compressed version of a set of documents with a minimum loss of relevant information.
