Improving Abstractive Document Summarization with Salient Information Modeling (P19-1)
Copied to clipboard
| Challenge: | Abstractive document summarization is a task of natural language generation which generates fluent summaries with salient information automatically. |
| Approach: | They propose to incorporate a Gaussian focal bias on attention scores into an encoder to enhance the perception of local context and to distinguish salient information precisely. |
| Outcome: | The proposed framework outperforms state-of-the-art models on the CNN/Daily Mail benchmark and is based on a focus-attention mechanism and two new extensions. |
Similar Papers
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. |
Attention Head Masking for Inference Time Content Selection in Abstractive Summarization (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing studies show that multi-heads attentions at the same layer collectively guide the summarization. |
| Approach: | They propose an inference-time attention head masking mechanism that works on encoder-decoder attentions to pinpoint salient content at inference time. |
| Outcome: | The proposed technique outperforms state-of-the-art models on CNN/DailyMail and New York Times datasets and is data-efficient. |
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization (D18-1)
Copied to clipboard
| Challenge: | Existing methods to summarize short texts using a neural encoder-decoder are limited and expensive to obtain. |
| Approach: | They propose to use a maximal marginal relevance method to select representative sentences from multi-document input and leverage an abstractive encoder-decoder model to fuse disparate sentences to an abstract. |
| Outcome: | The proposed method compares favorably to state-of-the-art extractive and abstractive approaches judged by automatic metrics and human assessors. |
Improving Neural Abstractive Document Summarization with Explicit Information Selection Modeling (D18-1)
Copied to clipboard
| Challenge: | Existing neural abstractive methods for document summarization are not effective for document summary. |
| Approach: | They propose to extend basic neural encoding-decoding framework with an information selection layer to explicitly model and optimize the information selection process in abstractive document summarization. |
| Outcome: | The proposed model outperforms state-of-the-art methods on document summarization tasks significantly. |
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents (N18-2)
Copied to clipboard
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian
| Challenge: | Existing abstractive summarization models focus on summarizing sentences and short documents. |
| Approach: | They propose a hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. |
| Outcome: | The proposed model significantly outperforms state-of-the-art models on two large-scale datasets of scientific papers. |
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. |
Transformer-based Model for Single Documents Neural Summarization (D19-56)
Copied to clipboard
| Challenge: | Existing approaches for document summarization use manual feature engineering, integer linear programming and data-driven approaches. |
| Approach: | They propose a framework that encodes the source text first with a transformer, then a sequence-to-sequence model. |
| Outcome: | The proposed framework improves performance on extractive and abstractive document summarization task using the CNN/DailyMail and Newsroom datasets. |
Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization (2021.naacl-main)
Copied to clipboard
Yichen Jiang, Asli Celikyilmaz, Paul Smolensky, Paul Soulos, Sudha Rao, Hamid Palangi, Roland Fernandez, Caitlin Smith, Mohit Bansal, Jianfeng Gao
| Challenge: | Abstractive summarization is the task of generating a concise summary of input documents . a middle-aged man and a young girl died after they were unable to avoid the plane . |
| Approach: | They propose a model that enriches the original Transformer with a Tensor Product Representation for abstractive summarization. |
| Outcome: | The proposed model outperforms the Transformer and the original TP-Transformer significantly on several datasets. |
Efficient Attentions for Long Document Summarization (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing models that use full attentions have quadratic computational and memory complexities, and are too costly for long documents. |
| Approach: | They propose an efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source. |
| Outcome: | The proposed model can process ten times more tokens than current models that use full attentions. |
Document Modeling with External Attention for Sentence Extraction (P18-1)
Copied to clipboard
Shashi Narayan, Ronald Cardenas, Nikos Papasarantopoulos, Shay B. Cohen, Mirella Lapata, Jiangsheng Yu, Yi Chang
| Challenge: | Document modeling is essential to a variety of natural language understanding tasks. |
| Approach: | They propose to use external information to improve document modeling for sentence extraction problems. |
| Outcome: | The proposed model outperforms baseline models on document summarization and answer selection tasks and achieves state-of-the-art results on WikiQA and NewsQA. |