Source-linked AI summary
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
Sandeep Subramanian, Raymond Li, Jonathan Pilault, Christopher Pal
TL;DR
Long documents pose a challenge for abstractive summarization because relevant content may exceed a transformer’s token window. The paper first extracts important sentences and then uses them to condition a single transformer language model, achieving stronger and more abstractive summarization results across several datasets while retaining factuality as an open challenge.
Problem
Abstractive summarization must generate coherent summaries from long scientific and other documents containing hundreds or thousands of words.
Method
The method extracts important sentences with hierarchical document models and uses them to condition a single GPT-like Transformer LM trained on formatted documents and summaries.
Results
The conditioned Transformer LM outperforms prior abstractive and mixed results on arXiv, PubMed, and BigPatent except on ROUGE-L, and performs strongly on Newsroom.
Takeaways & Limitations
The extractive step followed by abstractive generation produces high-quality summaries of long sequences and is less prone to copying entire phrases or sentences.
Takeaways & Limitations
Transformer-generated abstractive summaries can contain imaginary content, leaving factual faithfulness an open challenge.
Abstract
from arXiv · showhide
We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarization. We perform a simple extractive step before generating a summary, which is then used to condition the transformer language model on relevant information before being tasked with generating a summary. We show that this extractive step significantly improves summarization results. We also show that this approach produces more abstractive summaries compared to prior work that employs a copy mechanism while still achieving higher rouge scores. Note: The abstract above was not written by the authors, it was generated by one of the models presented in this paper.
Introduction
Long-document summarization requires models that can capture dependencies across hundreds or thousands of words. This work combines transformer language modeling with sentence extraction to generate abstractive summaries without an explicit encoder-decoder formulation.
- Introduction: Transformers offer shorter paths between inputs and outputs than RNNs, easing gradient flow and supporting long-term dependencies.RNNs are also difficult to optimize for long sequences and hard to parallelize.
- Introduction: Long scientific and news documents require encoding and generating hundreds or thousands of words, motivating models with large receptive fields.GPT-2 demonstrated that large transformers trained on substantial data can capture long-range dependencies.
- Introduction: The proposed system trains a single GPT-like Transformer LM on documents followed by summaries, then generates summaries conditioned on the document.It avoids the usual seq2seq encoder-decoder formulation.
- Introduction: The task is divided into extraction and abstraction: hierarchical models first select important sentences, which condition subsequent transformer-based generation.The paper uses two hierarchical document models for sentence extraction, including a pointer-network-based model and a sentence-classification model.
- Introduction: The authors report that transformer language models outperform typical seq2seq approaches without a copy mechanism and produce more abstractive summaries while achieving higher ROUGE scores.These are presented as the work’s two main contributions.
Related Work
Automatic summarization aims to reduce text while preserving important information and meaning. Related work contrasts extractive selection methods with abstractive generation, including copy-based mechanisms.
- Related Work: Extractive summarization selects salient words or sentences using features such as keywords, position, length, frequency, linguistic information, and similarity metrics.Prior approaches used structure-, vector-, and graph-based metrics.
- Related Work: Human summarizers interpret documents, prioritize important content, paraphrase concepts coherently, and generate diverse summaries.Extractive methods help identify relevant information but may lack human-like fluency and coherence.
- Related Work: Copy mechanisms allow abstractive models to reproduce source words, especially out-of-vocabulary terms, while coverage mechanisms reduce repetition.Attention scores can determine whether a word is generated or copied.
Framework
The framework combines hierarchical extractive sentence selection with a transformer language model conditioned on document context. For long documents, the model is trained on formatted document-summary sequences and generates summaries from introductions and extracted sentences.
- Framework: The model comprises independently trainable extractive and transformer language-model components.The extractor points to or classifies document sentences, while the transformer conditions on extracted sentences and part or all of the document.
- Extractive Models: The extractive component uses hierarchical sequence modeling to select important sentences from documents.It represents sentences with a token-level encoder and documents with a sentence-level encoder before decoding or classifying sentence selections.
- Extractive Models: The pointer extractor predicts the next sentence from document representations using an autoregressive LSTM decoder and attention.Beam search generates the extracted summary, with repeated sentence indices signaling the end of extraction.
- Extractive Models: The classifier-based extractor computes sentence-selection probabilities from hierarchical document representations and is trained with binary cross-entropy.The model uses a sigmoid output for sentence membership and optimizes against sentences in the gold-extracted summary.
- Transformer Language Models (TLM): Abstractive summarization is formulated with a single transformer language model trained from scratch on formatted document-summary sequences.The training format models the joint distribution of documents and summaries, enabling summary generation from the conditional distribution given a document.
- Transformer Language Models (TLM): For documents exceeding a transformer context window, the model uses the introduction and extracted sentences as generation context while the remaining paper supplies in-domain training data.The arXiv and PubMed examples are organized with the introduction, extracted sentences, and summary-related content in a fixed sequence.
Results and Analysis
Experiments span four large-scale long-document datasets and evaluate full-length F-1 ROUGE against extractive and abstractive baselines. The extractive-plus-transformer approach outperforms prior abstractive or mixed systems across several datasets, while copy mechanisms retain an advantage on long exact matches.
- Experimental setup: Four datasets—arXiv, PubMed, bigPatent, and Newsroom—are used to evaluate long-document summarization.The datasets are described as large-scale and long-document benchmarks.
- Experimental setup: Full-length F-1 ROUGE scores are used, with all reported ROUGE confidence intervals at most 0.24.The evaluation reuses code from Cohan et al. (2018).
- Results: The extractive models outperform previous extractive baselines on both the arXiv and PubMed datasets.Prior results are drawn from previously published benchmark evaluations.
- Results: The TLM-I+E (G,M) model outperforms prior abstractive or mixed results on arXiv, PubMed, and bigPatent, except on ROUGE-L.The comparison includes multiple extractive and abstractive systems, including pointer-generator and discourse-aware models.
- Results: On Newsroom, the method exceeds the attention-based abstractive model by a large margin and surpasses the pointer generator on abstractive and mixed outputs, while Exconsumm performs better.The authors attribute the weaker ROUGE-L result to the absence of a copy mechanism.
- Abstractiveness of generated abstracts: More than 10% of 20-grams from the pointing model’s abstracts also occur in the article, whereas the proposed model produces more abstractive summaries.The proposed model copies longer sequences when conditioned on extracted sentences, and most copied n-grams come from those sentences.
Conclusion
The paper demonstrates that an extractive step followed by an abstractive Transformer language model can summarize long texts effectively. The approach improves performance across several datasets and produces less copied summaries, while factual faithfulness remains unresolved.
- Conclusion: The extractive step improves results relative to an abstractive variant that conditions only on the input text.The comparison quantitatively measures the positive impact of adding extracted sentences before generation.
- Conclusion: The proposed summaries are less prone to copying entire phrases or sentences from the input than prior pointing-based methods.The paper reports that pointing models copy longer sequences more often, particularly at higher n-gram lengths.
- Conclusion: Evaluating scientific-article and patent summaries for factual correctness and coherence requires highly trained evaluators and substantial time.The authors note that such human studies may require significant resource investments.
- Conclusion: Developing summaries that preserve source facts while matching human creativity in coherent, concise synthesis remains an open challenge.The conclusion frames factual faithfulness and human-level synthesis as unresolved goals.
Samples from the arXiv test set
The supplied passages identify qualitative sample tables from the arXiv test set but do not describe the generated summaries themselves.
- Samples from the arXiv test set: The paper presents samples from TLM-I and TLM-I+E (G,M) on a random arXiv test example.The supplied material provides the table reference but no sample content or comparison.
T-SNE of learned word embeddings
The paper visualizes TLM-learned word embeddings with t-SNE and reports clustering associated with broad paper categories.
- T-SNE of learned word embeddings: Words associated with computer science and physics cluster in different regions of the learned embedding space.The visualization uses roughly 300 representative TF-IDF words per arXiv submission category.
- T-SNE of learned word embeddings: The t-SNE visualization suggests that the model partitions embedding space according to broad paper categories in which words frequently occur.This provides a qualitative view of category-related structure in the learned representations.