Bertsum Inter Sentence

BERTSUM with Inter-Sentence Transformer for Extractive Summarization

When employing BERTSUM for extractive summarization, a significant enhancement can be achieved by passing the BERT-generated sentence embeddings through an additional transformer encoder layer before feeding them into a sigmoid classifier. This architectural modification allows the model to learn document-level features by explicitly modeling the relationships between sentences, thereby improving the quality and coherence of the selected summary sentences.

Why Use a Transformer After BERT?

BERT excels at generating contextualized sentence representations. However, these initial embeddings, while rich in semantic meaning for individual sentences, are generated independently. In the context of extractive summarization, the ability to understand how sentences relate to one another within a document is paramount for selecting the most relevant and coherent set of sentences.

To address this, a transformer encoder is applied to the sequence of BERT sentence embeddings. This layer captures inter-sentence dependencies and learns global document-level features, enabling the model to grasp the overall structure and flow of the document.

Recap of the Transformer Encoder

A standard transformer encoder block is composed of several key components stacked together:

• Multi-Head Self-Attention: This mechanism allows the model to weigh the importance of different sentences in relation to each other, capturing dependencies across the entire sequence.

• Feed-Forward Neural Network (FNN): A position-wise fully connected network that processes the output of the attention layer.

• Layer Normalization (LN): Applied before or after sub-layers to stabilize training and improve convergence.

• Positional Embeddings: Added to the input embeddings to inject information about the order of sentences in the document, as self-attention itself is permutation-invariant.

Step-by-Step Process of BERTSUM with Transformer Encoder

The workflow for BERTSUM with an inter-sentence transformer can be broken down into the following steps:

Step 1: Obtain Sentence Representations from BERT

The initial step involves processing the input document through BERT to obtain contextualized embeddings for each sentence.

Let:

• T represent the sequence of BERT-generated sentence embeddings. If a document has n sentences, T will be a tensor of shape (n, d_model), where d_model is the hidden dimension of BERT.

Step 2: Add Positional Embeddings

To preserve the sequential information of the sentences, positional embeddings are added to the BERT embeddings.

Let:

• PosEmb denote the positional embeddings for each sentence.

• T_hat represent the enriched sentence embeddings.

$$T_{hat} = T + PosEmb$$

Step 3: Feed into Transformer Encoder

The enriched sentence embeddings (T_hat) are then passed through one or more transformer encoder layers. Each layer applies multi-head self-attention followed by a feed-forward network, with layer normalization applied at each step.

Let:

• Enc_l represent the output of the transformer encoder at layer l.

• MHAtt denote the Multi-Head Attention mechanism.

• FNN denote the Feed-Forward Network.

• LN denote Layer Normalization.

For any encoder layer l, the computation is as follows:

$$Enc_l = LN(FNN(LN(MHAtt(T_{hat}))))$$

This process is repeated for L stacked encoder layers, where the output of layer l-1 becomes the input for layer l.

Step 4: Output of the Top Encoder Layer

The final hidden state representation from the last (top) encoder layer is used for classification.

Let:

• T_prime represent the output of the final encoder layer (Enc_L).

$$T_{prime} = Enc_L$$

T_prime is a tensor of shape (n, d_model), where each row represents a sentence embedding that has been enriched by contextual information from all other sentences in the document.

Step 5: Classify with a Sigmoid Function

The final step involves using a sigmoid classifier to determine the probability of each sentence being included in the summary.

$$Prob = \text{Sigmoid}(T_{prime})$$

Here, Prob is a tensor of shape (n, 1), where each value represents the probability of the corresponding sentence being selected for the extractive summary.

Workflow Diagram (Textual Explanation)

The overall workflow can be visualized as follows:

1. Input Document $\rightarrow$ BERT $\rightarrow$ Sentence Embeddings (T)

2. Sentence Embeddings (T) + Positional Embeddings (PosEmb) $\rightarrow$ Enriched Embeddings ($T_{hat}$)

3. Enriched Embeddings ($T_{hat}$) $\rightarrow$ L x Transformer Encoder Layers $\rightarrow$ Final Hidden State ($T_{prime}$)

4. Final Hidden State ($T_{prime}$) $\rightarrow$ Sigmoid Classifier $\rightarrow$ Sentence Importance Score (Prob)

Conclusion

Integrating an inter-sentence transformer on top of BERT within the BERTSUM framework significantly enhances its capabilities for extractive summarization. By explicitly modeling the relationships and dependencies between sentences, the model gains a deeper understanding of the document's global context. This leads to the selection of summary sentences that are not only semantically relevant but also more coherent and contextually aware, ultimately improving the overall quality of the generated summaries.

SEO Keywords

• BERTSUM transformer encoder

• Inter-sentence transformer in summarization

• BERT sentence embeddings for extractive summarization

• Positional embeddings in document summarization

• BERTSUM architecture explained

• Transformer encoder after BERT

• Document-level summarization with BERT

• Self-attention in extractive summarization

Interview Questions

1. Why is it important to model inter-sentence relationships in extractive summarization?

   • Answer: Understanding how sentences relate to each other is crucial for identifying the most salient and coherent sentences that form a good summary. It helps in capturing the overall narrative flow and avoiding redundancy or abrupt transitions.

2. What limitations exist when using BERT sentence embeddings directly for classification?

   • Answer: BERT embeddings are generated independently for each sentence. This means they lack explicit information about the relationships and dependencies between sentences within the document, which is vital for selecting a cohesive summary.

3. How does adding a transformer encoder on top of BERT improve summarization performance?

   • Answer: The transformer encoder allows the model to learn document-level features by capturing inter-sentence dependencies through self-attention. This richer contextual understanding leads to better sentence selection.

4. What is the role of positional embeddings in BERTSUM with a transformer encoder?

   • Answer: Positional embeddings provide the transformer encoder with information about the order of sentences. Since self-attention mechanisms are permutation-invariant, these embeddings are essential for the model to understand the sequential structure of the document.

5. Explain the architecture of a single transformer encoder block.

   • Answer: A single transformer encoder block typically consists of a multi-head self-attention layer followed by a position-wise feed-forward neural network. Both sub-layers are usually wrapped with layer normalization and residual connections.

6. Why do we apply layer normalization before and after multi-head attention and feed-forward layers?

   • Answer: Layer normalization helps stabilize the learning process by normalizing the activations within each layer. This prevents vanishing or exploding gradients and allows for deeper networks and faster convergence. Applying it before (pre-norm) or after (post-norm) sub-layers, along with residual connections, is a common practice for robust training.

7. What does the equation Enc_l = LN(FNN(LN(MHAtt(T_hat)))) represent?

   • Answer: This equation describes the forward pass through a single transformer encoder layer. It shows that the input embeddings (T_hat for the first layer, or Enc_{l-1} for subsequent layers) first go through layer normalization and multi-head self-attention, then through another layer normalization, and finally through a feed-forward network. The output of this entire block is Enc_l.

8. How many transformer layers should be stacked on top of BERT for optimal performance?

   • Answer: The optimal number of layers is typically determined through experimentation and depends on the specific dataset and task. Common practices range from 1 to 4 transformer encoder layers.

9. Compare using a sigmoid classifier directly on BERT embeddings vs. after a transformer encoder.

   • Answer: Directly on BERT embeddings, the classifier only has access to individual sentence semantics without explicit inter-sentence context. After a transformer encoder, the classifier utilizes sentence representations that incorporate document-level context and relationships, leading to more informed and coherent sentence selection.

10. Can this architecture be adapted for multilingual summarization tasks? How?

    • Answer: Yes, this architecture can be adapted. One would need to use a multilingual BERT model (e.g., mBERT) to generate sentence embeddings for documents in different languages. The transformer encoder and classification head would then process these multilingual embeddings. Pre-training on massive multilingual corpora is key for mBERT's effectiveness.