Summarization
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Benchmarks
MuLD (VLSP)
Most implemented
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles
MuLD: The Multitask Long Document Benchmark
Sparsifying Transformer Models with Trainable Representation Pooling
Papers
Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles
Assessing the effectiveness of large language models (LLMs) in performing different tasks is crucial for understanding their strengths and weaknesses. This paper presents Hierarchical Prompting Taxonomy (HPT), grounded o…
Arithmetic ReasoningCode GenerationCommon Sense ReasoningGSM8K+8MuLD: The Multitask Long Document Benchmark
The impressive progress in NLP techniques has been driven by the development of multi-task benchmarks such as GLUE and SuperGLUE. While these benchmarks focus on tasks for one or two input sentences, there has been excit…
Question AnsweringStyle change detectionSummarizationText Classification+1Sparsifying Transformer Models with Trainable Representation Pooling
We propose a novel method to sparsify attention in the Transformer model by learning to select the most-informative token representations during the training process, thus focusing on the task-specific parts of an input.…
DecoderDocument SummarizationSummarizationText SummarizationEnsure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization
In this paper, we investigate the sentence summarization task that produces a summary from a source sentence. Neural sequence-to-sequence models have gained considerable success for this task, while most existing approac…
Abstractive Text SummarizationDecoderInformativenessSentence+3Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization
Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably. Inspired by the traditional template-based summarization approaches, this paper proposes t…
Abstractive Text SummarizationInformativenessRerankingSentence Summarization+1Faithful to the Original: Fact Aware Neural Abstractive Summarization
Unlike extractive summarization, abstractive summarization has to fuse different parts of the source text, which inclines to create fake facts. Our preliminary study reveals nearly 30% of the outputs from a state-of-the-…
Abstractive Text SummarizationExtractive SummarizationInformativenessOpen Information Extraction+1