Source-linked AI summary
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
Ramesh Nallapati, Bowen Zhou, Cicero Nogueira dos santos, Caglar Gulcehre, Bing Xiang
TL;DR
Abstractive summarization requires short, lossy paraphrases with weaker word-level alignment than translation. The paper applies attentional encoder-decoder RNNs, adds models for keywords, hierarchy, and rare words, and reports state-of-the-art results on two datasets while introducing a multi-sentence benchmark dataset. Its qualitative analysis identifies complex-sentence meaning as a remaining weakness.
Problem
Abstractive summarization must compress documents while preserving salient concepts, unlike translation’s largely lossless mapping and stronger word-level alignment.
Method
The paper applies an attentional encoder-decoder RNN and proposes feature-rich, switching generator-pointer, and hierarchical-attention models for summarization-specific challenges.
Results
The attentional encoder-decoder outperforms state-of-the-art systems on two different datasets, while proposed models provide further improvement and a new multi-sentence dataset establishes benchmarks.
Takeaways & Limitations
The work establishes attentional encoder-decoder RNNs and several extensions as effective approaches for abstractive summarization across different datasets and summary structures.
Takeaways & Limitations
Capturing the meaning of complex sentences remains a weakness, and generated summaries can sometimes misinterpret source semantics.
Abstract
from arXiv · showhide
In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora. We propose several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture, such as modeling key-words, capturing the hierarchy of sentence-to-word structure, and emitting words that are rare or unseen at training time. Our work shows that many of our proposed models contribute to further improvement in performance. We also propose a new dataset consisting of multi-sentence summaries, and establish performance benchmarks for further research.
1 Introduction
Abstractive summarization generates compressed paraphrases of salient document ideas, but differs from machine translation because summaries are short, lossy, and lack clear word-level alignment. The paper applies attentional encoder-decoder RNNs, proposes models for summarization-specific weaknesses, and introduces a multi-sentence dataset with benchmarks.
- Abstractive summarization generates short summaries that capture salient ideas through compressed paraphrasing rather than sentence extraction.
- Unlike machine translation, summarization produces short outputs whose length is relatively independent of the source document and intentionally loses information while preserving key concepts.
- Summarization has less obvious source-target word alignment than translation, making direct transfer of translation architectures insufficiently tailored to the task.
- The paper applies an attentional encoder-decoder RNN to summarization and reports performance exceeding state-of-the-art systems on two English corpora.
- It proposes novel models addressing summarization-specific problems and introduces a multi-sentence abstractive summarization dataset with benchmark results.
2 Models
The paper builds an attentional encoder-decoder baseline and extends it with feature-rich, pointer-based, and hierarchical architectures. These extensions target keywords, rare or unseen words, and document structure by enriching representations and controlling attention or copying.
- Baseline encoder-decoder: The baseline uses a bidirectional GRU encoder, a unidirectional GRU decoder, source attention, a target-vocabulary softmax, and a large-vocabulary trick.The mini-batch decoder vocabulary is restricted to words appearing in that batch’s source documents.
- Feature-rich encoder: The feature-rich encoder augments word embeddings with POS, named-entity, term-frequency, and inverse-document-frequency features to identify key concepts and entities.Continuous TF and IDF values are discretized into fixed bins and represented categorically before concatenation with word embeddings.
- Switching generator-pointer: The switching generator-pointer decoder chooses at each time step between generating a target-vocabulary word and pointing to a source position whose word is copied.The switch is modeled from the available decoding context, while the pointer distribution uses attention over document word positions.
- Switching generator-pointer: Pointer decisions use encoder hidden states and contextual representations, enabling the model to point accurately to rare or unseen words absent from the target vocabulary.At training time, explicit pointer information is supplied for summary words outside the target vocabulary; at test time, generation or pointing is selected automatically.
- Hierarchical attention: The hierarchical encoder uses bidirectional RNNs at word and sentence levels, jointly modeling key sentences and keywords through two-level attention.Word-level attention is reweighted by sentence-level attention, renormalized, and used to compute the decoder’s attention-weighted context; sentence positional embeddings model positional importance.
3 Related Work
Prior work spans extractive and abstractive summarization, with deep-learning systems increasingly using neural sequence models. This work extends RNN-based abstractive summarization with models and a dataset intended to address key limitations and support benchmarking.
- Most earlier summarization systems were extractive, reproducing selected source sentences or passages as summaries.
- Deep-learning approaches introduced convolutional and recurrent encoder-decoder models for abstractive summarization, reporting strong results on English, Chinese, Gigaword, and DUC data.
- RNN-based extractive summarization is not directly comparable because this work and related systems model abstractive rather than extractive summarization.
- The authors use RNNs for both source and target sequences, then extend the standard architecture with novel models addressing critical summarization problems.
- The feature-rich encoder adds linguistic and term-weighting features, while the switching generator-pointer model combines extractive and abstractive behavior in one end-to-end framework.
4 Experiments and Results
Experiments evaluate attentional encoder-decoder models on Gigaword, DUC, and CNN/Daily Mail, including a newly constructed multi-sentence corpus. Specialized models improve performance or reduce repetition, while the results establish preliminary baselines for future work.
- Gigaword: The Gigaword experiments compare baseline, hierarchical-attention, feature-enhanced, and pointer-based models using Rouge and source-copy rate.The baseline uses one source sentence; adding a second sentence, hierarchical attention, linguistic features, and a switching generator/pointer are evaluated as model variants.
- Gigaword: The best Gigaword model achieves statistically significant improvement on Rouge-1 over the comparable model of Chopra et al. (2016).
- DUC Corpus: On DUC, words-lvt5k-1sent outperforms RAS-Elman on two of three Rouge variants while remaining competitive on Rouge-1.Models are evaluated with limited-length Rouge recall, including comparison against ABS, ABS+, RAS-Elman, and TOPIARY.
- CNN/Daily Mail Corpus: The CNN/Daily Mail corpus contains 286,817 training pairs, 13,368 validation pairs, and 11,487 test pairs with multi-sentence summaries.It restores ordered summary bullets from CNN and Daily Mail stories, treating each bullet as a sentence.
- CNN/Daily Mail Corpus: Temporal attention significantly improves performance over baseline and hierarchical attention while producing fewer repetitive summary highlights.It down-weights current attention where past attention was already high, encouraging focus on uncovered document portions.
- CNN/Daily Mail Corpus: The CNN/Daily Mail results are preliminary baselines, and qualitative outputs show that complex-sentence semantics remain a weakness.Some outputs are meaningful despite Rouge mismatch, while others misinterpret sentence semantics.
5 Qualitative Analysis
The qualitative analysis finds that the models often generate meaningful, relevant summaries, but still struggle with semantic interpretation and repetition. The switching generator/pointer model accurately copies entities and phrases, yet does not significantly improve overall performance.
- Output quality: The best models often produce meaningful and relevant summaries, even when their wording differs from the target.This quality is not fully captured by Rouge-style word and phrase matching.
- Output quality: The models sometimes misinterpret complex sentence semantics and generate comical summaries.The authors identify understanding complex sentence meaning as a clear weakness.
- Generator/pointer behavior: The switching generator/pointer model accurately points to named entities and multi-word phrases, but its overall performance improvement is not significant.The authors suggest its impact may be larger in settings with more rare words.
- Multi-sentence summaries: On CNN/Daily Mail data, models produce good multi-sentence summaries but sometimes repeat the same sentence or phrase.Intra-attention is proposed as a way to encourage remembering previously generated words.
- Output quality: Good summaries are more prevalent than poor summaries in the displayed validation examples.The table intentionally shows equal numbers of good and poor examples, so prevalence is not represented by the display counts.
6 Conclusion
The paper applies attentional encoder-decoder models to abstractive summarization and proposes models targeting specific summarization problems. It reports significant improvements over state-of-the-art results on two datasets and introduces a multi-sentence benchmark dataset.
- Results: Attentional encoder-decoder models significantly outperform state-of-the-art results on two different datasets.The conclusion describes the results as very promising.
- Contributions: Each proposed model addresses a specific problem in abstractive summarization and yields further performance improvement.The conclusion presents these models as extensions beyond the basic attentional encoder-decoder.
- Dataset: The paper proposes a new dataset for multi-sentence summarization and establishes benchmark numbers for it.Future work focuses on building more robust models for summaries consisting of multiple sentences.