Papers Summarization

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…

MuLD: 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…

Sparsifying 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.…

Ensure 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…

Retrieve, 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…

Faithful 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 Sentence Summarization with Attentive Recurrent Neural Networks

Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora. We propose several nove…