Bertsum Lstm

BERTSUM with LSTM

This section explores the integration of a Long Short-Term Memory (LSTM) network into the BERTSUM architecture to enhance extractive summarization. Instead of directly using sentence representations from BERT, this approach feeds them through an LSTM layer to capture sequential and contextual relationships between sentences, which is crucial for generating coherent summaries.

Why Use LSTM After BERT?

BERT excels at generating rich, context-aware sentence embeddings by processing text in parallel. However, it does not inherently capture the explicit order of sentences within a document. By introducing an LSTM layer after BERT, we can:

• Learn Temporal Dynamics: LSTMs are designed to model sequences and can learn dependencies and flow between sentences.

• Capture Sequential Context: This allows the model to understand how sentences relate to each other temporally, which is vital for identifying the most important sentences that form a cohesive summary.

How BERTSUM with LSTM Works

The BERTSUM with LSTM architecture follows these steps:

Step 1: Obtain Sentence Embeddings from BERT

The process begins by using a pre-trained BERT model to generate embeddings for each sentence in the input document.

Let the input document be represented as a sequence of sentences: $T = [T_1, T_2, ..., T_n]$

Where $T_i$ is the vector representation (embedding) of the $i$-th sentence, generated by BERT.

Step 2: Feed Sentence Embeddings into an LSTM

Each sentence embedding $T_i$ is then passed sequentially through an LSTM layer.

$H_i = \text{LSTM}(T_i)$

• $T_i$: The input sentence embedding for sentence $i$.

• $H_i$: The hidden state output from the LSTM corresponding to sentence $i$.

The LSTM layer processes these embeddings chronologically, enabling it to capture dependencies between preceding and current sentences. This results in a context-enriched sentence embedding, $H_i$, which implicitly encodes the sentence's position and relationship within the document's sequence.

Step 3: Classification with a Sigmoid Layer

Finally, each context-enriched hidden state $H_i$ is fed into a sigmoid classifier. This classifier determines the probability of selecting sentence $i$ to be included in the final summary.

$P_i = \text{Sigmoid}(H_i)$

• $P_i$: The probability of selecting sentence $i$ for the summary.

A higher probability indicates that the sentence is more likely to be important for the summary.

Summary Workflow

The overall pipeline can be visualized as:

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

2. Sentence Embeddings ($T$) $\rightarrow$ LSTM $\rightarrow$ Hidden States ($H$)

3. Hidden States ($H$) $\rightarrow$ Sigmoid Classifier $\rightarrow$ Sentence Importance Scores ($P$)

Sentences with the highest importance scores are selected to form the extractive summary.

BERTSUM Configurations

BERTSUM can be configured in several ways for extractive summarization:

• BERTSUM with a Simple Sigmoid Classifier: Directly uses BERT sentence embeddings with a classifier.

• BERTSUM with an Inter-Sentence Transformer: Employs transformer encoder layers to explicitly model relationships between sentences.

• BERTSUM with LSTM: Utilizes an LSTM to model the sequential relationships between sentences.

Fine-Tuning BERT for Summarization

To adapt a pre-trained BERT model for extractive summarization, it's necessary to fine-tune the model along with the summarization layer (classifier, transformer, or LSTM). This fine-tuning process allows the model to specialize in identifying salient sentences for summarization.

The optimization is typically performed using the Adam optimizer. A common learning rate schedule that balances exploration and convergence is:

$\text{Learning Rate} = \frac{2 \times 10^{-3}}{1 + \text{decay_rate} \times \text{epoch}}$

(Replace decay_rate and epoch with appropriate values determined during experimentation.)

What's Next?

We have explored how BERT can be used for extractive summarization through simple classifiers, transformer layers, and LSTM networks. The next logical step is to consider generating new sentences for the summary, rather than just selecting existing ones. This leads us to the domain of Abstractive Summarization using BERT.