論文メモ Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
Ramesh Nallapati et al 2016
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Notes about
“Abstractive TextSummarization using Sequence-to-sequence RNNs and Beyond”
Introduction
Authors
- apply the attentional encoder-decoder RNN to text summarization.
- The model is developed for machine translation.
- propose the novel models and show that they provide additional improvement in performance.
- The models use additional linguistic features such as parts-of-speech and so on.
Text summarization
- Abstractive text summarization is the task of generating a headline or short summary consisting of a few sentences that captures the salient ideas of an article or a passage.
The difficulties of this kind of problems
- In summarization, original documents are compressed in lossy manner such that key concepts are preserved.
Models: RNN language model (preparation)
- (Extraction of embedding vector) $$\bar{y}_t = E y_{t-1}$$
- (Calculation of hidden layer) $$h_t = \tanh \left( W^{(l)} [\bar{y}_t, h_{t-1}]^t + b^{(l)} \right)$$
- (Calculation of output layrer) $$o_t = W^{(o)} h_t + b^{(o)}$$
- (Calculation of probability) $$p_t = \text{softmax}(o_t)$$
- (Extraction of probability) $$P(y_t| Y_{<t}) = p_t \cdot y_t$$
Models: bidirectional RNN language model (preparation)
- (Forward calculation of hidden layer)
- $$\overrightarrow{h}_t = \tanh \left( \overrightarrow{W}^{(l)} [\bar{y}_t, \overrightarrow{h}_{t-1}]^t + \overrightarrow{b}^{(l)} \right)$$
- (Backward calculation of hidden layer)
- $$\overleftarrow{h}_t = \tanh \left( \overleftarrow{W}^{(l)} [\bar{y}_t, \overleftarrow{h}_{t+1}]^t + \overleftarrow{b}^{(l)} \right)$$
- (Calculation of output layrer)
- $$o_t = W^{(o)} [\overrightarrow{h}_t, \overleftarrow{h}_t]^t + b^{(o)}$$
Models: encoder-decoder RNN language model (preparation)
- (Input) $$X=(x_i)_ {i=1}^I$$
- (Output) $$Y=(y_j)_ {j=0}^{J+1}$$ where $$y_0=BOS$$ and $$y_{J+1}=EOS$$
- (Encoder) $$z=\Lambda(X)$$
- (Hidden layer) $$h_t = \phi(h_{j-1}, y_{j-1})$$ where $$h_0 = z$$
- (Probability)
- $$P(Y|X) = \prod_{j=1}^{J+1} P(y_j|Y_{<j}, X)$$
- $$P(y_j|Y_{<j}, X) = \psi(h_j, y_j)$$
Models: Soft attention mechanism (preparation)
- (Encoder) $$h_i^{(s)} = \phi^{(s)}(x_i, h_{i-1}^{(s)})$$
- (Decoder) $$h_j^{(t)} = \phi^{(t)}(y_j, h_{j-1}^{(t)})$$
- (Attention)
- $$\bar{h} = \sum_{i=1}^I a_ih_i^{(s)}$$ where $$a_i = \frac{\exp(e_i)}{\sum_{\tilde{i}=1}^I \exp(e_{\tilde{i}})}$$ and $$e_i = \Omega(h_i^{(s)}, h_j^{(t)})$$
- $$\hat{h}_j^{(t)} = \tanh(W^{(a)}[\bar{h}, h_j^{(t)}])$$
Models: Large vocabulary trick (preparation)
Large vocabulary problems
- The computation cost of $$\text{softmax}(o_t)$$ is very high, when the vocabulary size is large, because $$\dim(o_t)$$ equals to the vocabulary size.
Approaches
- model-specific approaches
- noise-contrastive estimation
- binary hierarchical softmax
- translation-specific approaches
- model-specific approaches
Models: Authors apply encoder-decoder RNN with attention and large vocabulary trick(LVT)
- The encoder-decoder RNN with attention is described in preparation slides.
- Authors' LVT is that
- the decoder-vocabulary of each mini-batch is restricted to words in the source documents of that batch.
- the most frequent words in the target dictionary are added until the vocabulary reaches a fixes size.
Models: Authors propose the model uses the additional linguistic features.
- Linguistic features
- parts-of-speech tags (POS tag)
- named-entity tags
- MUC: organization, person, location, date, time, money, percent
- TF (Term Frequency)
- $$ tf_{i, j} = \frac{n_{i, j}}{\sum_k n_{k, j}}$$
- $$n_{i, j}$$ is the number of times that term $$t_i$$ occurs in document $$d_j$$.
- IDF (Inverse Document Frequency)
- $$ idf_{i}=\log \frac{|D|}{{d: t_i \in d}} $$
Models: Authors propose modeling rare/unseen words using switching generator-pointer.
- The algorithm is needed to handle unseen words, because the vocabulary is limited at training time.
- In general, a most common way is to emit an ‘UNK’ token as a placeholder.
- In summarization, an intuitive way is to simply point to their location in the source document.
- Authors model this notion using our novel switching decoder/pointer architecture.
- The switch decides between using the generator or a pointer at every time step.
Models: The switch
- The probability of the switch turning on at the i-th time-step is
- The pointer value at i-th time-step is
- $$p_i = \arg \max_{j} (Pa_i(j))$$ for $$j \in {1, …, N_d}$$
- where $$Pa_i(j) = \exp(va \cdot (W_ha h_{i-1} + Wa_e E o_{i-1} + W_ca hd_{j} + ba))$$
Models: The training of the switch parameters.
- At the training time, the explicit pointer information is provides whenever the summary word does not exist in the target vocabulary.
- The loss function is
- $$\log P(y|x) = \sum_{i} (g_i \log {P(y_i|y_{-i}, x) P(s_i)}$$ $$ + (1-g_i) \log {P(p(i)|y_{-i}, x)(1-P(s_i))})$$
- $$g_i$$ is an indicator function that is set to 0 whenever the word at position $$i$$ in the summary is OOV with respect to the decoder vocabulary.
Models: Capturing hierarchical document structure with hierarchical attention
- In summarization, it is important to identify the key sentences from which the summary can be drawn.
- This model aims to capture this notation of two levels of importance using bi-directional RNNs on the source sids, one at the word level and the other at the sentence level.
- $$Pa(j) = \frac{Pa_w(j) Pa_s(s(j))}{\sum_{k=1}^{N_d} Pa_w(k) Pa_s(s(k))}$$