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"""
@author: Nianlong Gu, Institute of Neuroinformatics, ETH Zurich
@email: nianlonggu@gmail.com
The source code for the paper: MemSum: Extractive Summarization of Long Documents using Multi-step Episodic Markov Decision Processes
When using this code or some of our pre-trained models for your application, please cite the following paper:
@article{DBLP:journals/corr/abs-2107-08929,
author = {Nianlong Gu and
Elliott Ash and
Richard H. R. Hahnloser},
title = {MemSum: Extractive Summarization of Long Documents using Multi-step
Episodic Markov Decision Processes},
journal = {CoRR},
volume = {abs/2107.08929},
year = {2021},
url = {https://arxiv.org/abs/2107.08929},
eprinttype = {arXiv},
eprint = {2107.08929},
timestamp = {Thu, 22 Jul 2021 11:14:11 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2107-08929.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import pickle
import numpy as np
class AddMask( nn.Module ):
def __init__( self, pad_index ):
super().__init__()
self.pad_index = pad_index
def forward( self, x):
# here x is a batch of input sequences (not embeddings) with the shape of [ batch_size, seq_len]
mask = x == self.pad_index
return mask
class PositionalEncoding( nn.Module ):
def __init__(self, embed_dim, max_seq_len = 512 ):
super().__init__()
self.embed_dim = embed_dim
self.max_seq_len = max_seq_len
pe = torch.zeros( 1, max_seq_len, embed_dim )
for pos in range( max_seq_len ):
for i in range( 0, embed_dim, 2 ):
pe[ 0, pos, i ] = math.sin( pos / ( 10000 ** ( i/embed_dim ) ) )
if i+1 < embed_dim:
pe[ 0, pos, i+1 ] = math.cos( pos / ( 10000** ( i/embed_dim ) ) )
self.register_buffer( "pe", pe )
## register_buffer can register some variables that can be saved and loaded by state_dict, but not trainable since not accessible by model.parameters()
def forward( self, x ):
return x + self.pe[ :, : x.size(1), :]
class MultiHeadAttention( nn.Module ):
def __init__(self, embed_dim, num_heads ):
super().__init__()
dim_per_head = int( embed_dim/num_heads )
self.ln_q = nn.Linear( embed_dim, num_heads * dim_per_head )
self.ln_k = nn.Linear( embed_dim, num_heads * dim_per_head )
self.ln_v = nn.Linear( embed_dim, num_heads * dim_per_head )
self.ln_out = nn.Linear( num_heads * dim_per_head, embed_dim )
self.num_heads = num_heads
self.dim_per_head = dim_per_head
def forward( self, q,k,v, mask = None):
q = self.ln_q( q )
k = self.ln_k( k )
v = self.ln_v( v )
q = q.view( q.size(0), q.size(1), self.num_heads, self.dim_per_head ).transpose( 1,2 )
k = k.view( k.size(0), k.size(1), self.num_heads, self.dim_per_head ).transpose( 1,2 )
v = v.view( v.size(0), v.size(1), self.num_heads, self.dim_per_head ).transpose( 1,2 )
a = self.scaled_dot_product_attention( q,k, mask )
new_v = a.matmul(v)
new_v = new_v.transpose( 1,2 ).contiguous()
new_v = new_v.view( new_v.size(0), new_v.size(1), -1 )
new_v = self.ln_out(new_v)
return new_v
def scaled_dot_product_attention( self, q, k, mask = None ):
## note the here q and k have converted into multi-head mode
## q's shape is [ Batchsize, num_heads, seq_len_q, dim_per_head ]
## k's shape is [ Batchsize, num_heads, seq_len_k, dim_per_head ]
# scaled dot product
a = q.matmul( k.transpose( 2,3 ) )/ math.sqrt( q.size(-1) )
# apply mask (either padding mask or seqeunce mask)
if mask is not None:
a = a.masked_fill( mask.unsqueeze(1).unsqueeze(1) , -1e9 )
# apply softmax, to get the likelihood as attention matrix
a = F.softmax( a, dim=-1 )
return a
class FeedForward( nn.Module ):
def __init__( self, embed_dim, hidden_dim ):
super().__init__()
self.ln1 = nn.Linear( embed_dim, hidden_dim )
self.ln2 = nn.Linear( hidden_dim, embed_dim )
def forward( self, x):
net = F.relu(self.ln1(x))
out = self.ln2(net)
return out
class TransformerEncoderLayer(nn.Module):
def __init__(self, embed_dim, num_heads, hidden_dim ):
super().__init__()
self.mha = MultiHeadAttention( embed_dim, num_heads )
self.norm1 = nn.LayerNorm( embed_dim )
self.feed_forward = FeedForward( embed_dim, hidden_dim )
self.norm2 = nn.LayerNorm( embed_dim )
def forward( self, x, mask, dropout_rate = 0. ):
short_cut = x
net = F.dropout(self.mha( x,x,x, mask ), p = dropout_rate)
net = self.norm1( short_cut + net )
short_cut = net
net = F.dropout(self.feed_forward( net ), p = dropout_rate )
net = self.norm2( short_cut + net )
return net
class TransformerDecoderLayer( nn.Module ):
def __init__(self, embed_dim, num_heads, hidden_dim ):
super().__init__()
self.masked_mha = MultiHeadAttention( embed_dim, num_heads )
self.norm1 = nn.LayerNorm( embed_dim )
self.mha = MultiHeadAttention( embed_dim, num_heads )
self.norm2 = nn.LayerNorm( embed_dim )
self.feed_forward = FeedForward( embed_dim, hidden_dim )
self.norm3 = nn.LayerNorm( embed_dim )
def forward(self, encoder_output, x, src_mask, trg_mask , dropout_rate = 0. ):
short_cut = x
net = F.dropout(self.masked_mha( x,x,x, trg_mask ), p = dropout_rate)
net = self.norm1( short_cut + net )
short_cut = net
net = F.dropout(self.mha( net, encoder_output, encoder_output, src_mask ), p = dropout_rate)
net = self.norm2( short_cut + net )
short_cut = net
net = F.dropout(self.feed_forward( net ), p = dropout_rate)
net = self.norm3( short_cut + net )
return net
class MultiHeadPoolingLayer( nn.Module ):
def __init__( self, embed_dim, num_heads ):
super().__init__()
self.num_heads = num_heads
self.dim_per_head = int( embed_dim/num_heads )
self.ln_attention_score = nn.Linear( embed_dim, num_heads )
self.ln_value = nn.Linear( embed_dim, num_heads * self.dim_per_head )
self.ln_out = nn.Linear( num_heads * self.dim_per_head , embed_dim )
def forward(self, input_embedding , mask=None):
a = self.ln_attention_score( input_embedding )
v = self.ln_value( input_embedding )
a = a.view( a.size(0), a.size(1), self.num_heads, 1 ).transpose(1,2)
v = v.view( v.size(0), v.size(1), self.num_heads, self.dim_per_head ).transpose(1,2)
a = a.transpose(2,3)
if mask is not None:
a = a.masked_fill( mask.unsqueeze(1).unsqueeze(1) , -1e9 )
a = F.softmax(a , dim = -1 )
new_v = a.matmul(v)
new_v = new_v.transpose( 1,2 ).contiguous()
new_v = new_v.view( new_v.size(0), new_v.size(1) ,-1 ).squeeze(1)
new_v = self.ln_out( new_v )
return new_v
class LocalSentenceEncoder( nn.Module ):
def __init__( self, vocab_size, pad_index, embed_dim, num_heads , hidden_dim , num_enc_layers , pretrained_word_embedding ):
super().__init__()
self.addmask = AddMask( pad_index )
self.rnn = nn.LSTM( embed_dim, embed_dim, 2, batch_first = True, bidirectional = True)
self.mh_pool = MultiHeadPoolingLayer( 2*embed_dim, num_heads )
self.norm_out = nn.LayerNorm( 2*embed_dim )
self.ln_out = nn.Linear( 2*embed_dim, embed_dim )
if pretrained_word_embedding is not None:
## make sure the pad embedding is 0
pretrained_word_embedding[pad_index] = 0
self.register_buffer( "word_embedding", torch.from_numpy( pretrained_word_embedding ) )
else:
self.register_buffer( "word_embedding", torch.randn( vocab_size, embed_dim ) )
"""
input_seq 's shape: batch_size x seq_len
"""
def forward( self, input_seq, dropout_rate = 0. ):
mask = self.addmask( input_seq )
## batch_size x seq_len x embed_dim
net = self.word_embedding[ input_seq ]
net, _ = self.rnn( net )
net = self.ln_out(F.relu(self.norm_out(self.mh_pool( net, mask ))))
return net
class GlobalContextEncoder(nn.Module):
def __init__(self, embed_dim, num_heads, hidden_dim, num_dec_layers ):
super().__init__()
# self.pos_encode = PositionalEncoding( embed_dim)
# self.layer_list = nn.ModuleList( [ TransformerEncoderLayer( embed_dim, num_heads, hidden_dim ) for _ in range(num_dec_layers) ] )
self.rnn = nn.LSTM( embed_dim, embed_dim, 2, batch_first = True, bidirectional = True)
self.norm_out = nn.LayerNorm( 2*embed_dim )
self.ln_out = nn.Linear( 2*embed_dim, embed_dim )
def forward(self, sen_embed, doc_mask, dropout_rate = 0.):
net, _ = self.rnn( sen_embed )
net = self.ln_out(F.relu( self.norm_out(net) ) )
return net
class ExtractionContextDecoder( nn.Module ):
def __init__( self, embed_dim, num_heads, hidden_dim, num_dec_layers ):
super().__init__()
self.layer_list = nn.ModuleList( [ TransformerDecoderLayer( embed_dim, num_heads, hidden_dim ) for _ in range(num_dec_layers) ] )
## remaining_mask: set all unextracted sen indices as True
## extraction_mask: set all extracted sen indices as True
def forward( self, sen_embed, remaining_mask, extraction_mask, dropout_rate = 0. ):
net = sen_embed
for layer in self.layer_list:
# encoder_output, x, src_mask, trg_mask , dropout_rate = 0.
net = layer( sen_embed, net, remaining_mask, extraction_mask, dropout_rate )
return net
class Extractor( nn.Module ):
def __init__( self, embed_dim, num_heads ):
super().__init__()
self.norm_input = nn.LayerNorm( 3*embed_dim )
self.ln_hidden1 = nn.Linear( 3*embed_dim, 2*embed_dim )
self.norm_hidden1 = nn.LayerNorm( 2*embed_dim )
self.ln_hidden2 = nn.Linear( 2*embed_dim, embed_dim )
self.norm_hidden2 = nn.LayerNorm( embed_dim )
self.ln_out = nn.Linear( embed_dim, 1 )
self.mh_pool = MultiHeadPoolingLayer( embed_dim, num_heads )
self.norm_pool = nn.LayerNorm( embed_dim )
self.ln_stop = nn.Linear( embed_dim, 1 )
self.mh_pool_2 = MultiHeadPoolingLayer( embed_dim, num_heads )
self.norm_pool_2 = nn.LayerNorm( embed_dim )
self.ln_baseline = nn.Linear( embed_dim, 1 )
def forward( self, sen_embed, relevance_embed, redundancy_embed , extraction_mask, dropout_rate = 0. ):
if redundancy_embed is None:
redundancy_embed = torch.zeros_like( sen_embed )
net = self.norm_input( F.dropout( torch.cat( [ sen_embed, relevance_embed, redundancy_embed ], dim = 2 ) , p = dropout_rate ) )
net = F.relu( self.norm_hidden1( F.dropout( self.ln_hidden1( net ) , p = dropout_rate ) ))
hidden_net = F.relu( self.norm_hidden2( F.dropout( self.ln_hidden2( net) , p = dropout_rate ) ))
p = self.ln_out( hidden_net ).sigmoid().squeeze(2)
net = F.relu( self.norm_pool( F.dropout( self.mh_pool( hidden_net, extraction_mask) , p = dropout_rate ) ))
p_stop = self.ln_stop( net ).sigmoid().squeeze(1)
net = F.relu( self.norm_pool_2( F.dropout( self.mh_pool_2( hidden_net, extraction_mask ) , p = dropout_rate ) ))
baseline = self.ln_baseline(net)
return p, p_stop, baseline
## naive tokenizer with just lower() function
class SentenceTokenizer:
def __init__(self ):
pass
def tokenize(self, sen ):
return sen.lower()
class Vocab:
def __init__(self, words, eos_token = "<eos>", pad_token = "<pad>", unk_token = "<unk>" ):
self.words = words
self.index_to_word = {}
self.word_to_index = {}
for idx in range( len(words) ):
self.index_to_word[ idx ] = words[idx]
self.word_to_index[ words[idx] ] = idx
self.eos_token = eos_token
self.pad_token = pad_token
self.unk_token = unk_token
self.eos_index = self.word_to_index[self.eos_token]
self.pad_index = self.word_to_index[self.pad_token]
self.tokenizer = SentenceTokenizer()
def index2word( self, idx ):
return self.index_to_word.get( idx, self.unk_token)
def word2index( self, word ):
return self.word_to_index.get( word, -1 )
# The sentence needs to be tokenized
def sent2seq( self, sent, max_len = None , tokenize = True):
if tokenize:
sent = self.tokenizer.tokenize(sent)
seq = []
for w in sent.split():
if w in self.word_to_index:
seq.append( self.word2index(w) )
if max_len is not None:
if len(seq) >= max_len:
seq = seq[:max_len -1]
seq.append( self.eos_index )
else:
seq.append( self.eos_index )
seq += [ self.pad_index ] * ( max_len - len(seq) )
return seq
def seq2sent( self, seq ):
sent = []
for i in seq:
if i == self.eos_index or i == self.pad_index:
break
sent.append( self.index2word(i) )
return " ".join(sent)
class MemSum:
def __init__( self, model_path, vocabulary_path, gpu = None ):
## max_doc_len is used to truncate too long sentence into at most 100 words
max_seq_len =100
## max_doc_len is used to truncate too long document into at most 200 sentences
max_doc_len = 200
## These parameters below have been fintuned for the pretrained model
embed_dim=200
num_heads=8
hidden_dim = 1024
N_enc_l = 2
N_enc_g = 2
N_dec = 3
with open( vocabulary_path , "rb" ) as f:
words = pickle.load(f)
self.vocab = Vocab( words )
vocab_size = len(words)
self.local_sentence_encoder = LocalSentenceEncoder( vocab_size, self.vocab.pad_index, embed_dim,num_heads,hidden_dim,N_enc_l, None )
self.global_context_encoder = GlobalContextEncoder( embed_dim, num_heads, hidden_dim, N_enc_g )
self.extraction_context_decoder = ExtractionContextDecoder( embed_dim, num_heads, hidden_dim, N_dec )
self.extractor = Extractor( embed_dim, num_heads )
ckpt = torch.load( model_path, map_location = "cpu" )
self.local_sentence_encoder.load_state_dict( ckpt["local_sentence_encoder"] )
self.global_context_encoder.load_state_dict( ckpt["global_context_encoder"] )
self.extraction_context_decoder.load_state_dict( ckpt["extraction_context_decoder"] )
self.extractor.load_state_dict(ckpt["extractor"])
self.device = torch.device( "cuda:%d"%(gpu) if gpu is not None and torch.cuda.is_available() else "cpu" )
self.local_sentence_encoder.to(self.device)
self.global_context_encoder.to(self.device)
self.extraction_context_decoder.to(self.device)
self.extractor.to(self.device)
self.sentence_tokenizer = SentenceTokenizer()
self.max_seq_len = max_seq_len
self.max_doc_len = max_doc_len
def get_ngram(self, w_list, n = 4 ):
ngram_set = set()
for pos in range(len(w_list) - n + 1 ):
ngram_set.add( "_".join( w_list[ pos:pos+n] ) )
return ngram_set
def extract( self, document_batch, p_stop_thres , ngram_blocking , ngram, return_sentence_position, return_sentence_score_history, max_extracted_sentences_per_document ):
"""document_batch is a batch of documents:
[ [ sen1, sen2, ... , senL1 ],
[ sen1, sen2, ... , senL2], ...
]
"""
## tokenization:
document_length_list = []
sentence_length_list = []
tokenized_document_batch = []
for document in document_batch:
tokenized_document = []
for sen in document:
tokenized_sen = self.sentence_tokenizer.tokenize( sen )
tokenized_document.append( tokenized_sen )
sentence_length_list.append( len(tokenized_sen.split()) )
tokenized_document_batch.append( tokenized_document )
document_length_list.append( len(tokenized_document) )
max_document_length = self.max_doc_len
max_sentence_length = self.max_seq_len
## convert to sequence
seqs = []
doc_mask = []
for document in tokenized_document_batch:
if len(document) > max_document_length:
# doc_mask.append( [0] * max_document_length )
document = document[:max_document_length]
else:
# doc_mask.append( [0] * len(document) +[1] * ( max_document_length - len(document) ) )
document = document + [""] * ( max_document_length - len(document) )
doc_mask.append( [ 1 if sen.strip() == "" else 0 for sen in document ] )
document_sequences = []
for sen in document:
seq = self.vocab.sent2seq( sen, max_sentence_length )
document_sequences.append(seq)
seqs.append(document_sequences)
seqs = np.asarray(seqs)
doc_mask = np.asarray(doc_mask) == 1
seqs = torch.from_numpy(seqs).to(self.device)
doc_mask = torch.from_numpy(doc_mask).to(self.device)
extracted_sentences = []
sentence_score_history = []
p_stop_history = []
with torch.no_grad():
num_sentences = seqs.size(1)
sen_embed = self.local_sentence_encoder( seqs.view(-1, seqs.size(2) ) )
sen_embed = sen_embed.view( -1, num_sentences, sen_embed.size(1) )
relevance_embed = self.global_context_encoder( sen_embed, doc_mask )
num_documents = seqs.size(0)
doc_mask = doc_mask.detach().cpu().numpy()
seqs = seqs.detach().cpu().numpy()
extracted_sentences = []
extracted_sentences_positions = []
for doc_i in range(num_documents):
current_doc_mask = doc_mask[doc_i:doc_i+1]
current_remaining_mask_np = np.ones_like(current_doc_mask ).astype(np.bool_) | current_doc_mask
current_extraction_mask_np = np.zeros_like(current_doc_mask).astype(np.bool_) | current_doc_mask
current_sen_embed = sen_embed[doc_i:doc_i+1]
current_relevance_embed = relevance_embed[ doc_i:doc_i+1 ]
current_redundancy_embed = None
current_hyps = []
extracted_sen_ngrams = set()
sentence_score_history_for_doc_i = []
p_stop_history_for_doc_i = []
for step in range( max_extracted_sentences_per_document+1 ) :
current_extraction_mask = torch.from_numpy( current_extraction_mask_np ).to(self.device)
current_remaining_mask = torch.from_numpy( current_remaining_mask_np ).to(self.device)
if step > 0:
current_redundancy_embed = self.extraction_context_decoder( current_sen_embed, current_remaining_mask, current_extraction_mask )
p, p_stop, _ = self.extractor( current_sen_embed, current_relevance_embed, current_redundancy_embed , current_extraction_mask )
p_stop = p_stop.unsqueeze(1)
p = p.masked_fill( current_extraction_mask, 1e-12 )
sentence_score_history_for_doc_i.append( p.detach().cpu().numpy() )
p_stop_history_for_doc_i.append( p_stop.squeeze(1).item() )
normalized_p = p / p.sum(dim=1, keepdims = True)
stop = p_stop.squeeze(1).item()> p_stop_thres #and step > 0
#sen_i = normalized_p.argmax(dim=1)[0]
_, sorted_sen_indices =normalized_p.sort(dim=1, descending= True)
sorted_sen_indices = sorted_sen_indices[0]
extracted = False
for sen_i in sorted_sen_indices:
sen_i = sen_i.item()
if sen_i< len(document_batch[doc_i]):
sen = document_batch[doc_i][sen_i]
else:
break
sen_ngrams = self.get_ngram( sen.lower().split(), ngram )
if not ngram_blocking or len( extracted_sen_ngrams & sen_ngrams ) < 1:
extracted_sen_ngrams.update( sen_ngrams )
extracted = True
break
if stop or step == max_extracted_sentences_per_document or not extracted:
extracted_sentences.append( [ document_batch[doc_i][sen_i] for sen_i in current_hyps if sen_i < len(document_batch[doc_i]) ] )
extracted_sentences_positions.append( [ sen_i for sen_i in current_hyps if sen_i < len(document_batch[doc_i]) ] )
break
else:
current_hyps.append(sen_i)
current_extraction_mask_np[0, sen_i] = True
current_remaining_mask_np[0, sen_i] = False
sentence_score_history.append(sentence_score_history_for_doc_i)
p_stop_history.append( p_stop_history_for_doc_i )
results = [extracted_sentences]
if return_sentence_position:
results.append( extracted_sentences_positions )
if return_sentence_score_history:
results+=[sentence_score_history , p_stop_history ]
if len(results) == 1:
results = results[0]
return results
## document is a list of sentences
def summarize( self, document, p_stop_thres = 0.7, max_extracted_sentences_per_document = 10, return_sentence_position = False ):
sentences, sentence_positions = self.extract( [document], p_stop_thres, ngram_blocking = False, ngram = 0, return_sentence_position = True, return_sentence_score_history = False, max_extracted_sentences_per_document = max_extracted_sentences_per_document )
try:
sentences, sentence_positions = list(zip(*sorted( zip( sentences[0], sentence_positions[0] ), key = lambda x:x[1] )))
except ValueError:
sentences, sentence_positions = (), ()
if return_sentence_position:
return sentences, sentence_positions
else:
return sentences
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