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import nltk |
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import re |
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import nltkmodule |
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from newspaper import Article |
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from newspaper import fulltext |
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import requests |
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import itertools |
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import os |
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from nltk.tokenize import word_tokenize |
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from sentence_transformers import SentenceTransformer |
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import pandas as pd |
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import numpy as np |
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from pandas import ExcelWriter |
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from torch.utils.data import DataLoader |
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import math |
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from sentence_transformers import models, losses |
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from sentence_transformers import SentencesDataset, LoggingHandler, SentenceTransformer |
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from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator |
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from sentence_transformers.readers import * |
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from nltk.corpus import stopwords |
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stop_words = stopwords.words('english') |
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import matplotlib.pyplot as plt |
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from sklearn.cluster import KMeans |
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from sklearn.decomposition import PCA |
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from sklearn.metrics.pairwise import cosine_similarity |
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import scipy.spatial |
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import networkx as nx |
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from nltk.tokenize import sent_tokenize |
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import scispacy |
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import spacy |
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import en_core_sci_lg |
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import string |
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from nltk.stem.wordnet import WordNetLemmatizer |
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import gradio as gr |
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import inflect |
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from sklearn.cluster import KMeans |
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from sklearn.cluster import AgglomerativeClustering |
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from sklearn.metrics import silhouette_samples, silhouette_score, davies_bouldin_score |
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import json |
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from xml.etree import ElementTree as ET |
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p = inflect.engine() |
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nlp = en_core_sci_lg.load() |
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sp = en_core_sci_lg.load() |
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all_stopwords = sp.Defaults.stop_words |
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os.environ["TOKENIZERS_PARALLELISM"] = "false" |
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def remove_stopwords(sen): |
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sen_new = " ".join([i for i in sen if i not in stop_words]) |
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return sen_new |
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def keyphrase_generator(article_link, model_1, model_2, max_num_keywords, model_3, max_retrieved, model_4): |
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word_embedding_model = models.Transformer(model_3) |
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pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), |
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pooling_mode_mean_tokens=True, |
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pooling_mode_cls_token=False, |
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pooling_mode_max_tokens=False) |
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embedder = SentenceTransformer(modules=[word_embedding_model, pooling_model]) |
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element=[] |
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cluster_list_final=[] |
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comb_list=[] |
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comb=[] |
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title_list=[] |
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titles_list=[] |
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abstracts_list=[] |
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silhouette_score_list=[] |
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final_textrank_list=[] |
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document=[] |
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text_doc=[] |
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final_list=[] |
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score_list=[] |
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sum_list=[] |
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model_1 = SentenceTransformer(model_1) |
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model_2 = SentenceTransformer(model_2) |
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url = article_link |
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html = requests.get(url).text |
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article = fulltext(html) |
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corpus=sent_tokenize(article) |
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indicator_list=['concluded','concludes','in a study', 'concluding','conclude','in sum','in a recent study','therefore','thus','so','hence', |
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'as a result','accordingly','consequently','in short','proves that','shows that','suggests that','demonstrates that','found that','observed that', |
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'indicated that','suggested that','demonstrated that'] |
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count_dict={} |
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for l in corpus: |
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c=0 |
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for l2 in indicator_list: |
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if l.find(l2)!=-1: |
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c=1 |
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break |
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if c: |
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count_dict[l]=1 |
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else: |
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count_dict[l]=0 |
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for sent, score in count_dict.items(): |
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score_list.append(score) |
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clean_sentences_new = pd.Series(corpus).str.replace("[^a-zA-Z]", " ", regex = True).tolist() |
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corpus_embeddings = model_1.encode(clean_sentences_new) |
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sim_mat = np.zeros([len(clean_sentences_new), len(clean_sentences_new)]) |
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for i in range(len(clean_sentences_new)): |
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len_embeddings=(len(corpus_embeddings[i])) |
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for j in range(len(clean_sentences_new)): |
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if i != j: |
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if(len_embeddings == 1024): |
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sim_mat[i][j] = cosine_similarity(corpus_embeddings[i].reshape(1,1024), corpus_embeddings[j].reshape(1,1024))[0,0] |
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elif(len_embeddings == 768): |
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sim_mat[i][j] = cosine_similarity(corpus_embeddings[i].reshape(1,768), corpus_embeddings[j].reshape(1,768))[0,0] |
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nx_graph = nx.from_numpy_array(sim_mat) |
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scores = nx.pagerank(nx_graph, max_iter = 1500) |
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sentences=((scores[i],s) for i,s in enumerate(corpus)) |
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for elem in sentences: |
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element.append(elem[0]) |
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for sc, lst in zip(score_list, element): |
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sum1=sc+lst |
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sum_list.append(sum1) |
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x=sorted(((sum_list[i],s) for i,s in enumerate(corpus)), reverse=True) |
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for elem in x: |
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final_textrank_list.append(elem[1]) |
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a=int((10*len(final_textrank_list))/100.0) |
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if(a<5): |
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total=5 |
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else: |
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total=int(a) |
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for i in range(total): |
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document.append(final_textrank_list[i]) |
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doc=" ".join(document) |
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for i in document: |
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doc_1=nlp(i) |
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text_doc.append([X.text for X in doc_1.ents]) |
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entity_list = [item for sublist in text_doc for item in sublist] |
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entity_list = [word for word in entity_list if not word in all_stopwords] |
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entity_list = [word_entity for word_entity in entity_list if(p.singular_noun(word_entity) == False)] |
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entity_list=list(dict.fromkeys(entity_list)) |
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doc_embedding = model_2.encode([doc]) |
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candidates=entity_list |
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candidate_embeddings = model_2.encode(candidates) |
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distances = cosine_similarity(doc_embedding, candidate_embeddings) |
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top_n = max_num_keywords |
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keyword_list = [candidates[index] for index in distances.argsort()[0][-top_n:]] |
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keywords = '\n'.join(keyword_list) |
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c_len=(len(keyword_list)) |
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keyword_embeddings = embedder.encode(keyword_list) |
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data_embeddings = embedder.encode(keyword_list) |
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for num_clusters in range(1, top_n): |
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clustering_model = KMeans(n_clusters=num_clusters) |
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clustering_model.fit(keyword_embeddings) |
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cluster_assignment = clustering_model.labels_ |
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clustered_sentences = [[] for i in range(num_clusters)] |
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for sentence_id, cluster_id in enumerate(cluster_assignment): |
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clustered_sentences[cluster_id].append(keyword_list[sentence_id]) |
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cl_sent_len=(len(clustered_sentences)) |
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list_cluster=list(clustered_sentences) |
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a=len(list_cluster) |
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cluster_list_final.append(list_cluster) |
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if (c_len==cl_sent_len and c_len>=3) or cl_sent_len==1: |
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silhouette_avg = 0 |
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silhouette_score_list.append(silhouette_avg) |
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elif c_len==cl_sent_len==2: |
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silhouette_avg = 1 |
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silhouette_score_list.append(silhouette_avg) |
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else: |
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silhouette_avg = silhouette_score(keyword_embeddings, cluster_assignment) |
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silhouette_score_list.append(silhouette_avg) |
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res_dict = dict(zip(silhouette_score_list, cluster_list_final)) |
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cluster_items=res_dict[max(res_dict)] |
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for i in cluster_items: |
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z=' OR '.join(i) |
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comb.append("("+z+")") |
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comb_list.append(comb) |
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combinations = [] |
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for subset in itertools.combinations(comb, 2): |
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combinations.append(subset) |
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f1_list=[] |
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for s in combinations: |
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final = ' AND '.join(s) |
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f1_list.append("("+final+")") |
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f_1=' OR '.join(f1_list) |
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final_list.append(f_1) |
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ncbi_url='https://eutils.ncbi.nlm.nih.gov/entrez/eutils/' |
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last_url='esearch.fcgi?db=pubmed'+'&term='+f_1 |
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overall_url=ncbi_url+last_url+'&rettype=json'+'&sort=relevance' |
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pubmed_search_request = requests.get(overall_url) |
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root = ET.fromstring(pubmed_search_request.text) |
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levels = root.findall('.//Id') |
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search_id_list=[] |
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for level in levels: |
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name = level.text |
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search_id_list.append(name) |
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all_search_ids = ','.join(search_id_list) |
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fetch_url='efetch.fcgi?db=pubmed' |
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search_id='&id='+all_search_ids |
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return_url=ncbi_url+fetch_url+search_id+'&rettype=text'+'&retmode=xml'+'&retmax=500'+'&sort=relevance' |
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pubmed_abstract_request = requests.get(return_url) |
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root_1 = ET.fromstring(pubmed_abstract_request.text) |
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article_title = root_1.findall('.//ArticleTitle') |
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for a in article_title: |
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article_title_name = a.text |
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titles_list.append(article_title_name) |
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article_abstract = root_1.findall('.//AbstractText') |
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for b in article_abstract: |
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article_abstract_name = b.text |
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abstracts_list.append(article_abstract_name) |
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first_article = Article(url, language='en') |
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first_article.download() |
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first_article.parse() |
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article_heading=(first_article.title) |
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article_heading=sent_tokenize(article_heading) |
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model_4 = SentenceTransformer(model_4) |
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my_dict = dict(zip(titles_list,abstracts_list)) |
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title_embeddings = model_4.encode(titles_list) |
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heading_embedding = model_4.encode(article_heading) |
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similarities = cosine_similarity(heading_embedding, title_embeddings) |
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max_n = max_retrieved |
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sorted_titles = [titles_list[index] for index in similarities.argsort()[0][-max_n:]] |
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sorted_abstract_list=[] |
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for list_elem in sorted_titles: |
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sorted_abstract_list.append(my_dict[list_elem]) |
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sorted_dict = {'Title': sorted_titles, 'Abstract': sorted_abstract_list} |
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df_new=pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in sorted_dict.items() ])) |
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df_final = df_new.fillna(' ') |
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return sorted_dict |
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igen_pubmed = gr.Interface(keyphrase_generator, |
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inputs=[gr.components.Textbox(lines=1, placeholder="Provide article web link here (Can be chosen from examples below)",default="", label="Article web link"), |
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gr.components.Dropdown(choices=['sentence-transformers/all-mpnet-base-v2', |
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'sentence-transformers/all-mpnet-base-v1', |
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'sentence-transformers/all-distilroberta-v1', |
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'sentence-transformers/gtr-t5-large', |
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'pritamdeka/S-Bluebert-snli-multinli-stsb', |
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'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb', |
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'pritamdeka/S-BioBert-snli-multinli-stsb', |
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'sentence-transformers/stsb-mpnet-base-v2', |
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'sentence-transformers/stsb-roberta-base-v2', |
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'sentence-transformers/stsb-distilroberta-base-v2', |
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'sentence-transformers/sentence-t5-large', |
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'sentence-transformers/sentence-t5-base'], |
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type="value", |
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default='sentence-transformers/stsb-roberta-base-v2', |
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label="Select any SBERT model for TextRank from the list below"), |
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gr.components.Dropdown(choices=['sentence-transformers/paraphrase-mpnet-base-v2', |
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'sentence-transformers/all-mpnet-base-v1', |
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'sentence-transformers/paraphrase-distilroberta-base-v1', |
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'sentence-transformers/paraphrase-xlm-r-multilingual-v1', |
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'sentence-transformers/paraphrase-multilingual-mpnet-base-v2', |
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'sentence-transformers/paraphrase-albert-small-v2', |
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'sentence-transformers/paraphrase-albert-base-v2', |
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'sentence-transformers/paraphrase-MiniLM-L12-v2', |
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'sentence-transformers/paraphrase-MiniLM-L6-v2', |
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'sentence-transformers/all-MiniLM-L12-v2', |
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'sentence-transformers/all-distilroberta-v1', |
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'sentence-transformers/paraphrase-TinyBERT-L6-v2', |
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'sentence-transformers/paraphrase-MiniLM-L3-v2', |
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'sentence-transformers/all-MiniLM-L6-v2'], |
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type="value", |
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default='sentence-transformers/all-mpnet-base-v1', |
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label="Select any SBERT model for keyphrases from the list below"), |
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gr.components.Slider(minimum=5, maximum=20, step=1, default=10, label="Max Keywords"), |
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gr.components.Dropdown(choices=['cambridgeltl/SapBERT-from-PubMedBERT-fulltext', |
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'cambridgeltl/SapBERT-from-PubMedBERT-fulltext-mean-token'], |
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type="value", |
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default='cambridgeltl/SapBERT-from-PubMedBERT-fulltext', |
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label="Select any SapBERT model for clustering from the list below"), |
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gr.components.Slider(minimum=5, maximum=15, step=1, default=10, label="PubMed Max Abstracts"), |
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gr.components.Dropdown(choices=['pritamdeka/S-Bluebert-snli-multinli-stsb', |
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'pritamdeka/S-BioBert-snli-multinli-stsb', |
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'pritamdeka/S-Biomed-Roberta-snli-multinli-stsb', |
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'sentence-transformers/all-mpnet-base-v2'], |
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type="value", |
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default='sentence-transformers/all-mpnet-base-v2', |
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label="Select any SBERT model for abstracts from the list below")], |
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outputs=gr.outputs.JSON(label="Title and Abstracts"), |
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theme="peach", layout="horizontal", |
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title="PubMed Abstract Retriever", description="Retrieves relevant PubMed abstracts for an online article which can be used as further references. The output is in the form of JSON with <b><i>Title</i></b> and <b><i>Abstract</i></b> as the fields of the JSON output. Please note that it may take sometime for the models to load. Examples are provided below for demo purposes. Choose any one example to see the results. The models can be changed to see different results. ", |
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article= "This work is based on the paper <a href=https://dl.acm.org/doi/10.1145/3487664.3487701>provided here</a>." |
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"\t It uses the TextRank algorithm with SBERT to first find the top sentences and then extracts the keyphrases from those sentences using scispaCy and SBERT." |
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"\t The application then uses a UMLS based BERT model, <a href=https://arxiv.org/abs/2010.11784>SapBERT</a> to cluster the keyphrases using K-means clustering method and finally create a boolean query. After that the top k titles and abstracts are retrieved from PubMed database and displayed according to relevancy. The SapBERT models can be changed as per the list provided. " |
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"\t The list of SBERT models required in the textboxes can be found in <a href=www.sbert.net/docs/pretrained_models.html>SBERT Pre-trained models hub</a>." |
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"\t The model names can be changed from the list of pre-trained models provided. " |
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"\t The value of keyphrases can be changed. The default value is 10, minimum is 5 and a maximum value of 20. " |
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"\t The value of maximum abstracts to be retrieved can be changed. The minimum is 5, default is 10 and a maximum of 15.") |
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igen_pubmed.launch(share=False,server_name='0.0.0.0',show_error=True) |
