Papers with extractive and abstractive models
A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss (P18-1)
Copied to clipboard
| Challenge: | extractive models can obtain sentence-level attention with high ROUGE scores but less readable. abstractive models generate novel words and phrases not copied from the source text. |
| Approach: | They propose to combine extractive and abstractive models to achieve a unified model that generates readable paragraphs with word-level attention. |
| Outcome: | The proposed model achieves state-of-the-art ROUGE scores while being the most informative and readable summarization on the CNN/Daily Mail dataset in a solid human evaluation. |
MReD: A Meta-Review Dataset for Structure-Controllable Text Generation (2022.findings-acl)
Copied to clipboard
| Challenge: | a new text generation dataset is needed to controllable text summarization, but it lacks the domain knowledge. |
| Approach: | They propose to use existing text generation datasets to leverage input and control signals . they propose to annotate each meta-review sentence manually with a control signal . |
| Outcome: | The proposed method can be used to control the structure of a text generation dataset . it can be applied to a variety of tasks, including a task with a large number of meta-review sentences . |
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. |
Text Summarization with Pretrained Encoders (D19-1)
Copied to clipboard
| Challenge: | Existing pretraining languages such as ELMo and GPT have advanced a wide range of tasks. |
| Approach: | They propose a novel document-level encoder based on BERT which can express the semantics of a document and obtain representations for its sentences. |
| Outcome: | The proposed model achieves state-of-the-art in extractive and abstractive settings. |