| Challenge: | Abstractive text summarization models do not capture the abstractive nature of high quality summaries. |
| Approach: | They propose to decompose a decoder into a contextual network and a pretrained language model that incorporates prior knowledge about language generation. |
| Outcome: | The proposed model achieves comparable results to state-of-the-art models, based on ROUGE scores and human evaluations, while producing a significantly higher level of abstraction. |
Similar Papers
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. |
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. |
Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization (P19-1)
Copied to clipboard
| Challenge: | Abstractive text summarization is a demanding, time expensive and generally laborious task. |
| Approach: | They propose a framework for enhancing abstractive text summarization using deep learning techniques and semantic data transformations. |
| Outcome: | The proposed method is evaluated on two popular datasets with encouraging results. |
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. |
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 . |
Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution (2021.acl-long)
Copied to clipboard
| Challenge: | Abstractive summarization models have made great strides in recent years, but little is known about how they actually form summaries and how to understand where their decisions come from. |
| Approach: | They propose a two-step method to interpret summarization model decisions by categorizing each decoder decision into one of several generation modes. |
| Outcome: | The proposed method can identify phrases the summarization model has memorized and determine where in the training pipeline this memorization happened, and study complex generation phenomena on a per-instance basis. |
Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation (D18-1)
Copied to clipboard
| Challenge: | Recent work on abstractive summarization has made progress with neural encoder-decoder architectures, but these models lack explicit semantic modeling of the source document and its summary. |
| Approach: | They extend previous work on abstractive summarization using Abstract Meaning Representation (AMR) with a neural language generation stage which they guide using the source document. |
| Outcome: | The proposed approach improves summarization performance by 7.4 and 10.5 points in ROUGE-2 using gold standard AMR parses and parses obtained from an off-the-shelf parser respectively. |
Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples (N19-1)
Copied to clipboard
| Challenge: | Neural abstractive summarization systems generate summary texts conditioned on the input source text, and have recently achieved high ROUGE scores on benchmark summarizing datasets. |
| Approach: | They propose to analyze existing neural abstractive summarization systems by comparing their performance to human-written summaries. |
| Outcome: | The proposed systems perform better than human-written summarizations on different datasets and show that they are able to understand deeper syntactic and semantic structures. |
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. |
Entity-level Factual Consistency of Abstractive Text Summarization (2021.eacl-main)
Copied to clipboard
Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, Bing Xiang
| Challenge: | Existing models exhibit entity hallucination, generating names of entities that are not present in the source document. |
| Approach: | They propose to use entity-level factual consistency to improve model quality . they propose to filter the training data to reduce entity hallucination problem . |
| Outcome: | The proposed model can reduce the entity hallucination problem by filtering the training data. |