Papers with extractive summarizers
Extractive Summarization with Text Generator (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing extractive systems lack gold training signals, thereby hindering learning of extractive models. |
| Approach: | They propose to use text generators to train extractive summarizers by approximating outputs of abstractive summaries. |
| Outcome: | The proposed method can be used to train extractive summarizers without training . it is shown that the approximated summaries correlate positively with the auxiliary summary outputs. |
Extractive Summarization with SWAP-NET: Sentences and Words from Alternating Pointer Networks (P18-1)
Copied to clipboard
| Challenge: | Abstractive summarization methods use factual and grammatical errors to generate summaries. |
| Approach: | They propose a neural sequence-to-sequence model for extractive summarization called SWAP-NET . it identifies salient sentences and key words in an input document and combines them to form an extractive summary. |
| Outcome: | The proposed model outperforms state-of-the-art extractive summarization methods on large scale corpora. |
What Have We Achieved on Text Summarization? (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for text summarization have been investigated, but there are still gaps between them and human professionals. |
| Approach: | They analyze 8 major sources of errors on 10 representative summarization models manually. |
| Outcome: | Aiming to gain more understanding of summarization systems with respect to their strengths and limitations on a fine-grained syntactic and semantic level, we use 8 major sources of errors on 10 representative summarizing models. |
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. |
Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing extractive summarization methods focus on balancing salience and redundancy between sentences. |
| Approach: | They propose a hierarchical attentive heterogeneous graph for text summarization that models sentences . they propose to iteratively refine the sentence representations and deliver the labels by message passing . |
| Outcome: | The proposed method outperforms existing extractive summarization methods on large corpus. |
Post-Editing Extractive Summaries by Definiteness Prediction (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Abstract: Extractive summarization has been the mainstay of automatic summarizing for decades, but it still suffers from coreference issues arising from extracting sentences away from their original context. |
| Approach: | They propose a post-editing step that generates linguistic decisions that lead to improved extractive summaries by predicting definiteness of noun phrases. |
| Outcome: | The proposed system generates linguistic decisions that improve the quality of the extractive summaries. |