| Challenge: | a paper abstract writing system can automatically generate an abstract from a title . a typical recurrent neural network (RNN) based approach easily loses focus. |
| Approach: | They propose a paper abstract writing system that automatically generates an abstract from a title. |
| Outcome: | The proposed system passes Turing tests by junior domain experts and non-experts at a rate up to 80%. |
Similar Papers
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 . |
PaperRobot: Incremental Draft Generation of Scientific Ideas (P19-1)
Copied to clipboard
| Challenge: | a paper robot can read existing papers and create new nodes or links in the knowledge graphs. |
| Approach: | They propose to automate the creation of new ideas by predicting links from the background KGs. |
| Outcome: | The proposed paper automates three tasks: read existing papers, create new ideas, predict links . the paper generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time. |
How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing (D19-1)
Copied to clipboard
| Challenge: | Extensive experiments on a large-scale real-world text summarization dataset show that PESG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations. |
| Approach: | They propose a model that learns summary patterns and prototype facts from a prototype document . they use a fact checker to estimate mutual information between the input document and generated summary . |
| Outcome: | Experiments on a large-scale real-world text summarization dataset show that PESG achieves state-of-the-art performance. |
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing (P19-1)
Copied to clipboard
| Challenge: | Current sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation. |
| Approach: | They propose a sentence simplification model that learns explicit edit operations via a neural programmer-interpreter approach. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89 WikiSmall, -1.41 Newsela) |
Deep Copycat Networks for Text-to-Text Generation (D19-1)
Copied to clipboard
| Challenge: | Text-to-text generation tasks require copying words from the input to the output. |
| Approach: | They propose a transformer-based pointer network for text-to-text generation which generates more abstractive summaries and a further extension of this architecture for automatic post-editing. |
| Outcome: | The proposed model outperforms existing models in text-to-text generation tasks and improves translation accuracy. |
To Point or Not to Point: Understanding How Abstractive Summarizers Paraphrase Text (2021.findings-acl)
Copied to clipboard
| Challenge: | Abstractive summarization models have seen great improvements in recent years, but there is limited understanding of the strategies different models employ and how they relate their understanding of language. |
| Approach: | They characterize how one popular abstractive model uses an explicit copy/generation switch to control its level of abstraction vs extraction . they find that abstractive summarization models lack the semantic understanding necessary to generate paraphrases that are both abstractive and faithful to the source document. |
| Outcome: | The proposed model uses syntactic boundaries to truncate sentences that are often copied verbatim. |
Authorship Attribution for Neural Text Generation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in deep learning have enabled the generation of realistic artifacts . however, the qualities of texts generated by these models are better, often confusing classifiers if they are not real. |
| Approach: | They propose to use neural network-based language models to generate realistic texts . they investigate the authorship attribution problem in three versions of a text . |
| Outcome: | The proposed models generate texts that are difficult to distinguish from human-written ones . the results show that most generators still generate texts significantly different from human ones compared to other models . |
An Editorial Network for Enhanced Document Summarization (D19-54)
Copied to clipboard
| Challenge: | Existing extractive and abstractive summarization methods are less fluent, coherent and readable, whereas extractive methods are sensitive to vocabulary size, making them more difficult to train and generalize. |
| Approach: | They propose an approach which uses a combination of extractive and abstractive methods to combine a given sequence of sentences into a short version. |
| Outcome: | The proposed method is compared with state-of-the-art methods using extractive-only or abstractive- only baselines. |
Beyond Abstracts: A New Dataset, Prompt Design Strategy and Method for Biomedical Synthesis Generation (2024.acl-srw)
Copied to clipboard
| Challenge: | Existing methods to automate systematic reviews of papers are slow and incomplete . authors propose a new method to automating the systematic review process . |
| Approach: | They propose a method for automatic synthesis generation using a dataset and prompting-based method. |
| Outcome: | The proposed method improves the existing model and prompts the system to generate high-quality syntheses. |