Controlling Length in Abstractive Summarization Using a Convolutional Neural Network (D18-1)
Copied to clipboard
| Challenge: | Convolutional neural networks (CNNs) can't generate summaries of desired lengths due to space or length constraints. |
| Approach: | They propose an approach to constrain the summary length by extending a convolutional sequence to sequence model. |
| Outcome: | The proposed model outperforms baseline models in terms of ROUGE score, length variations and semantic similarity. |
Similar Papers
Abstractive Document Summarization with Summary-length Prediction (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing abstractive summarization models do not consider summarizing-specific information such as the target summary length. |
| Approach: | They propose a method for enabling a model to understand summarization-specific information by predicting the summary length in the encoder and generating a summary of the predicted length in fine-tuning. |
| Outcome: | The proposed method improves ROUGE scores on the WikiHow, NYT, and CNN/DM datasets. |
Length Control in Abstractive Summarization by Pretraining Information Selection (2022.acl-long)
Copied to clipboard
| Challenge: | Existing length-controllable summarization models generate summaries as long as training data . current methods only control lengths at decoding stage, but adapt to desired lengths . |
| Approach: | They propose a length-aware attention mechanism to adapt the encoding of the source based on the desired length. |
| Outcome: | The proposed method produces high-quality summaries with desired lengths and even those short lengths never seen in the training data. |
Global Encoding for Abstractive Summarization (P18-2)
Copied to clipboard
| Challenge: | Existing models for abstractive summarization suffer from repetition and semantic irrelevance. |
| Approach: | They propose a global encoding framework which controls the information flow from the encoder to the decoder based on the global information of the source context. |
| Outcome: | The proposed model outperforms baseline models on the LCSTS and English Gigaword and can generate summary of higher quality and reduce repetition. |
A New Approach to Overgenerating and Scoring Abstractive Summaries (2021.naacl-main)
Copied to clipboard
| Challenge: | Abstractive summarization is a learning objective to produce system outputs that resemble reference summaries on a word-to-word basis. |
| Approach: | They propose a two-staged strategy to generate multiple variants of the target summary and score and select admissible ones according to users’ needs. |
| Outcome: | The proposed approach can achieve state-of-the-art on benchmark summarization datasets. |
Positional Encoding to Control Output Sequence Length (N19-1)
Copied to clipboard
| Challenge: | Neural encoder-decoder models have been successful in natural language generation tasks, but they must be limited to a specified length for abstractive summarization. |
| Approach: | They propose a sinusoidal positional extension to preserve the length constraint so that a neural encoder-decoder model can generate a text of any length even if the target length is unseen in training data. |
| Outcome: | The proposed method can generate a text of any length even if the target length is unseen in training data and improves ROUGE scores. |
Abstractive Summarizers are Excellent Extractive Summarizers (2023.acl-short)
Copied to clipboard
| Challenge: | Abstractive summarization systems have traditionally been fragmented, limiting the benefits of compatible models. |
| Approach: | They propose three new inference algorithms using sequence-to-sequence architectures to model extractive summarization with an abstractive summmarization system. |
| Outcome: | The proposed algorithms outperform existing models on CNN and Dailymail and show that they are more efficient than existing models. |
On the Abstractiveness of Neural Document Summarization (D18-1)
Copied to clipboard
| Challenge: | Recent studies show that document summarization systems are abstractive . authors suggest that automated summarizing systems could be improved . |
| Approach: | They propose to use a pure copy system to verify abstractiveness of document summarization systems. |
| Outcome: | The proposed system produces abstractive summaries while being far more efficient. |
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 . |
Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing models to summarize texts without ground-truth summaries are extractive, which remove words from texts and thus are less flexible than abstractive models. |
| Approach: | They propose an unsupervised model that extracts words from texts and makes them mutually enhance each other. |
| Outcome: | The proposed model outperforms both abstractive and extractive models, while generating new words not contained in input texts. |
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to summarize documents are not extractive and require an abstractive approach. |
| Approach: | They propose a novel abstractive model which is conditioned on the article’s topics and based entirely on convolutional neural networks. |
| Outcome: | The proposed model outperforms an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans. |