Long-Span Summarization via Local Attention and Content Selection (2021.acl-long)
Copied to clipboard
| Challenge: | Transformer-based models are state-of-the-art for a wide range of natural language processing tasks, including document summarization. |
| Approach: | They exploit large pre-trained transformer-based models and address long-span dependencies in abstractive summarization using two methods: local self-attention; and explicit content selection. |
| Outcome: | The proposed models achieve state-of-the-art on Spotify Podcast, arXiv, and PubMed datasets. |
Similar Papers
Extractive Summarization of Long Documents by Combining Global and Local Context (D19-1)
Copied to clipboard
| Challenge: | Existing methods for extractive and abstractive summarization are far from human performance. |
| Approach: | They propose a neural single-document extractive summarization model for long documents that incorporates both the global context of the whole document and the local context. |
| Outcome: | The proposed model outperforms previous models on ROUGE-1, ROUGEE-2 and METEOR scores on two datasets of scientific papers. |
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models (2020.emnlp-main)
Copied to clipboard
| Challenge: | We present a method to produce abstractive summaries of documents that exceed several thousand words . we compare transformer based methods to extractive methods, but extractive models score higher . |
| Approach: | They propose a method to generate abstractive summaries of documents that exceed several thousand words via neural abstractive summary. |
| Outcome: | The proposed method produces abstractive summaries of documents that exceed several thousand words . it is compared with baseline methods, state-of-the-art models and variants of the proposed method . |
Content Selection in Deep Learning Models of Summarization (D18-1)
Copied to clipboard
| Challenge: | Using deep learning models, we find that word embedding does not improve performance over simpler models. |
| Approach: | They propose to use sentence embedding to perform content selection across multiple domains . they propose to propose two alternative models that use auto-regressive sentence extraction . |
| Outcome: | The proposed models improve performance across news, personal stories, meetings, and medical articles. |
Highlight-Transformer: Leveraging Key Phrase Aware Attention to Improve Abstractive Multi-Document Summarization (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing models do not consider key phrases in determining attention weights of self-attention . Existing work does not consider the importance of key phrases when determining weights . |
| Approach: | They propose a model with highlighting mechanism to assign greater attention weights to key phrases . they propose two structures of highlighting attention for each head and the multihead highlighting . experimental results show that their proposed model significantly outperforms the baseline model . |
| Outcome: | The proposed model outperforms the baseline models on a multi-news dataset. |
HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for summarizing semantic graph structure from raw text are cumbersome and inefficient for long-text documents. |
| Approach: | They propose a Transformer-based pre-trained model with multi-granularity sparse attentions for long-text extractive summarization. |
| Outcome: | The proposed model performs state-of-the-art on single- and multi-document summarization tasks while using less memory and fewer parameters. |
LongT5: Efficient Text-To-Text Transformer for Long Sequences (2022.findings-naacl)
Copied to clipboard
| Challenge: | Recent work has shown that increasing the input length or increasing model size can improve the performance of Transformer-based neural models. |
| Approach: | They propose a model that integrates attention ideas from long-input transformers and adopts pre-training strategies from summarization pre-train into the scalable T5 architecture. |
| Outcome: | The proposed model outperforms the original T5 models on several summarization and question answering tasks and achieves state-of-the-art results. |
Bottom-Up Abstractive Summarization (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to summarize text using end-to-end content selectors have had mixed success in content selection, for example copying full sentences from the source document. |
| Approach: | They propose to use content selectors to over-determine phrases in a source document that should be part of the summary. |
| Outcome: | The proposed model over-determines phrases in a source document that should be part of the summary while generating fluent summaries. |
Revisiting Transformer-based Models for Long Document Classification (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent literature in text classification is biased towards short text sequences . multi-page multi-paragraph documents cannot be efficiently encoded by vanilla transformers based on short text. |
| Approach: | They compare different Transformer-based Long Document Classification approaches to mitigate the computational overhead of vanilla transformers to encode much longer text. |
| Outcome: | The proposed models can process longer text and provide practical advice for long document classification tasks. |
Sparsity and Sentence Structure in Encoder-Decoder Attention of Summarization Systems (2021.emnlp-main)
Copied to clipboard
| Challenge: | Training and inference using large transformer models can be computationally expensive because the self-attention's time and memory grow quadratically with sequence length. |
| Approach: | They propose a modified transformer architecture that constrains the encoder-decoder attention mechanism to a subset of input sentences while maintaining system performance. |
| Outcome: | The proposed architecture can be trained and inferenced using large transformer models with expensive training and induction costs. |
The NLP Task Effectiveness of Long-Range Transformers (2023.eacl-main)
Copied to clipboard
| Challenge: | Existing benchmarks on long-range attention models have not been sufficient to develop efficient Transformers and their practical application on complex NLP tasks. |
| Approach: | They propose to benchmark 7 Transformer variants on 5 difficult NLP tasks and 7 datasets to examine their capacity for long-range attention. |
| Outcome: | The proposed models have advantages on content selection and query-guided decoding, but they come with previously unrecognized drawbacks such as insufficient attention to distant tokens and accumulated approximation error. |