给出基于编码器与解码器的文本自动摘要模型代码

以下是一个基于编码器-解码器的文本自动摘要模型的示例代码（使用 Keras 库）：

from keras.layers import Input, LSTM, Embedding, Dense
from keras.models import Model

# 定义编码器
encoder_inputs = Input(shape=(None, ))
enc_emb = Embedding(vocab_size, latent_dim, trainable=True)(encoder_inputs)
encoder_lstm = LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder_lstm(enc_emb)
encoder_states = [state_h, state_c]

# 定义解码器
decoder_inputs = Input(shape=(None, ))
dec_emb_layer = Embedding(vocab_size, latent_dim, trainable=True)
dec_emb = dec_emb_layer(decoder_inputs)
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(dec_emb, initial_state=encoder_states)
decoder_dense = Dense(vocab_size, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)

# 模型训练
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit([encoder_input_data, decoder_input_data], decoder_target_data, batch_size=batch_size, epochs=epochs, validation_split=0.2)

# 编码器推理模型
encoder_model = Model(encoder_inputs, encoder_states)

# 解码器推理模型
decoder_state_input_h = Input(shape=(latent_dim,))
decoder_state_input_c = Input(shape=(latent_dim,))
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
dec_emb2= dec_emb_layer(decoder_inputs)
decoder_outputs2, state_h2, state_c2 = decoder_lstm(dec_emb2, initial_state=decoder_states_inputs)
decoder_states2 = [state_h2, state_c2]
decoder_outputs2 = decoder_dense(decoder_outputs2)
decoder_model = Model([dec