Deep recurrent generative decoder for abstractive text summarization

Nov 29, 20171 like432 views

小町研・EMNLP読み会発表者：小平　知範タイトル：Deep recurrent generative decoder for abstractive text summarization 論文著者：Piji Li,Wai Lam, Lidong Bing, Zihao Wang

Deep Recurrent Generative
Decoder for Abstractive
Text Summarization
Piji Li, Wai Lam, Lidong Bing and Zihao Wang
Presentator: 小平知範

Abstract
• Propose abstractive text summarization based on a seq2seq
oriented enc-dec model equipped with a deep reccurent
genarative decoder (DRGN).
• DRGN achieves improvements over the sota.
• Data: LCSTS, GIGA word, DUC-2004

Introduction
• People may naturally follow some inherent structure when they
write the abstractive summaries.
• some common structures: 
- “What-Happened” 
- “Who Action What” etc.

Proposed
• Incorporate the latent structure information of summaries into
the abstractive summarization model.
• They employ VAEs (Kingma and Welling, 13, Rezende et al.,
14) as the base model for their generative framework.
• Inspired by (Chung et al., 15), they add historical dependencies
on the latent valiables of VAEs and propose a DRGD.

Contributions
• 1. They porpose a seq2seq oriented encdec model equipped with
a DRGD to model and learn structure information.
• 2. Both the generative latent structural information and
discriminative deterministic variables are jointly considered in
the generation process the abs summaries.
• 3. sota

input 
X
Output 
Y
GRU
VAE
inference
generation
• source text representation he ∈ Rkh.
• a series of hidden states {hd1,hd2,…hdn,}
Framework Description 
Overview

• Their proposed latent structure modeling framework can be
divided into two parts: 
inference (variational-encoder) and generation (valiational-decoder)
• For the task of Summarization, the previous latent structure
information needs to be considered for constructing effective
representations for the generation of next state.
Framework Description 
Recurrent Generative Decoder

• latent structure variable: zt
• a lower bound (the objective 
to be maximized on the  
marginal likelihood.

Abstractive Summary
Generation
• Bidirectional GRU Encoder.
• The decoder:  
discriminative deterministic decoding 
generative latent structure modeling.

Abstractive Summary Generation 
Discriminative deterministic decoding
• The first hidden state = hd1 = 1/Te ∑ ht
• Two layers of GRUs: 
1st: hd1t = GRU1(yt-1, hd1t-1) 
2nd: hd1t = GRU2(yt-1, hd2t-1, ct) {ct: using hd1t and hei)

Abstractive Summary Generation 
Generative latent structure modeling.
• henc: 
• µ: 
• logσ2 
• ε
• z:  
g is sigmoid function

Abstractive Summary Generation 
Generative latent structure modeling.
• hdec: 
• output: 
 
ς is softmax

Experimental Setup
Datasets
• Gigawords: Input: first sentence. Output: headline
• DUC-2004: Input: Document. Output: 75 bytes
• LCSTS: Input: Short text (such as tweet). Output: summary

Experimental Setup
Evaluation Metrics
• F-measures  
ROUGE-1 (R-1) 
ROUGE-2 (R-2) 
ROUGE-L (R-L) 
ROUGE-SU4 (R-SU4)

Evaluation
• TOPIARY (Zajic et al., 04) : compressive text summarization
• MOSES+ (Rush et al., 15), ABS and ABS+ (Rush et al., 15)
• RNN and RNN-context (Hu et al., 15)
• CopyNet (Gu et al., 16), RNN-distract (Chen et al., 16)
• RAS-LSTM, RAS-Elman (Chopra et al., 16),
• LenEmb (Kikuchi et al, 16), ASC+FSC (Miao and Blunsom, 16)
• lvt2k-1sent and lvt5k-1sent (Nallapati et al., 16)

Experimental Settings
• Gigawords:  
word embeddings: 300 
hidden states and latent vaiables: 500 
Maximum length. Input 100, Output 50 
Batch size : 256
• DUC-2004: 
Maximum length. Output 75 bytes
• LCSTS: 
word embeddings: 350 
hidden states and latent vaiables: 500 
Maximum length. Input: 120, Output 25 
Batch size 256
• Beam: 10, Adadelta(ρ = 0.95, ε = 1e-6)

Conclusions
• They propose a DRGD to improve performance.
• The model is a seq2seq oriented encdec framework equipped
with latent structure modeling component.
• summaries are generated based on the latent variables and the
deterministic states.
• sota

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Deep recurrent generative decoder for abstractive text summarization

1. Deep Recurrent Generative Decoder for Abstractive Text Summarization Piji Li, Wai Lam, Lidong Bing and Zihao Wang Presentator: 小平知範

2. Abstract • Propose abstractive text summarization based on a seq2seq oriented enc-dec model equipped with a deep reccurent genarative decoder (DRGN). • DRGN achieves improvements over the sota. • Data: LCSTS, GIGA word, DUC-2004

3. Introduction • People may naturally follow some inherent structure when they write the abstractive summaries. • some common structures:  - “What-Happened”  - “Who Action What” etc.

4. Proposed • Incorporate the latent structure information of summaries into the abstractive summarization model. • They employ VAEs (Kingma and Welling, 13, Rezende et al., 14) as the base model for their generative framework. • Inspired by (Chung et al., 15), they add historical dependencies on the latent valiables of VAEs and propose a DRGD.

5. Contributions • 1. They porpose a seq2seq oriented encdec model equipped with a DRGD to model and learn structure information. • 2. Both the generative latent structural information and discriminative deterministic variables are jointly considered in the generation process the abs summaries. • 3. sota

6. input  X Output  Y GRU VAE inference generation • source text representation he ∈ Rkh. • a series of hidden states {hd1,hd2,…hdn,} Framework Description  Overview

7. • Their proposed latent structure modeling framework can be divided into two parts:  inference (variational-encoder) and generation (valiational-decoder) • For the task of Summarization, the previous latent structure information needs to be considered for constructing effective representations for the generation of next state. Framework Description  Recurrent Generative Decoder

8. • latent structure variable: zt • a lower bound (the objective  to be maximized on the   marginal likelihood.

9. Abstractive Summary Generation • Bidirectional GRU Encoder. • The decoder:   discriminative deterministic decoding  generative latent structure modeling.

10. Abstractive Summary Generation  Discriminative deterministic decoding • The first hidden state = hd1 = 1/Te ∑ ht • Two layers of GRUs:  1st: hd1t = GRU1(yt-1, hd1t-1)  2nd: hd1t = GRU2(yt-1, hd2t-1, ct) {ct: using hd1t and hei)

11. Abstractive Summary Generation  Generative latent structure modeling. • henc:  • µ:  • logσ2  • ε • z:   g is sigmoid function

12. Abstractive Summary Generation  Generative latent structure modeling. • hdec:  • output:    ς is softmax

13. Learning • Likelihood term

14. Experimental Setup Datasets • Gigawords: Input: first sentence. Output: headline • DUC-2004: Input: Document. Output: 75 bytes • LCSTS: Input: Short text (such as tweet). Output: summary

15. Experimental Setup Evaluation Metrics • F-measures   ROUGE-1 (R-1)  ROUGE-2 (R-2)  ROUGE-L (R-L)  ROUGE-SU4 (R-SU4)

16. Evaluation • TOPIARY (Zajic et al., 04) : compressive text summarization • MOSES+ (Rush et al., 15), ABS and ABS+ (Rush et al., 15) • RNN and RNN-context (Hu et al., 15) • CopyNet (Gu et al., 16), RNN-distract (Chen et al., 16) • RAS-LSTM, RAS-Elman (Chopra et al., 16), • LenEmb (Kikuchi et al, 16), ASC+FSC (Miao and Blunsom, 16) • lvt2k-1sent and lvt5k-1sent (Nallapati et al., 16)

17. Experimental Settings • Gigawords:   word embeddings: 300  hidden states and latent vaiables: 500  Maximum length. Input 100, Output 50  Batch size : 256 • DUC-2004:  Maximum length. Output 75 bytes • LCSTS:  word embeddings: 350  hidden states and latent vaiables: 500  Maximum length. Input: 120, Output 25  Batch size 256 • Beam: 10, Adadelta(ρ = 0.95, ε = 1e-6)

18. Rouge Evaluation Gigawords

19. Rouge Evaluation DUC-2004

20. Rouge Evaluation LCSTS

21. Summary Case Analysis

22. Conclusions • They propose a DRGD to improve performance. • The model is a seq2seq oriented encdec framework equipped with latent structure modeling component. • summaries are generated based on the latent variables and the deterministic states. • sota

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