Maximal Marginal Relevance
¶
Calculate Maximal Marginal Relevance (MMR) between candidate keywords and the document.
MMR considers the similarity of keywords/keyphrases with the document, along with the similarity of already selected keywords and keyphrases. This results in a selection of keywords that maximize their within diversity with respect to the document.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
doc_embedding |
ndarray |
The document embeddings |
required |
word_embeddings |
ndarray |
The embeddings of the selected candidate keywords/phrases |
required |
words |
List[str] |
The selected candidate keywords/keyphrases |
required |
top_n |
int |
The number of keywords/keyhprases to return |
5 |
diversity |
float |
How diverse the select keywords/keyphrases are. Values between 0 and 1 with 0 being not diverse at all and 1 being most diverse. |
0.8 |
Returns:
Type | Description |
---|---|
List[Tuple[str, float]] |
The selected keywords/keyphrases with their distances |
Source code in keybert\_mmr.py
def mmr(
doc_embedding: np.ndarray,
word_embeddings: np.ndarray,
words: List[str],
top_n: int = 5,
diversity: float = 0.8,
) -> List[Tuple[str, float]]:
"""Calculate Maximal Marginal Relevance (MMR)
between candidate keywords and the document.
MMR considers the similarity of keywords/keyphrases with the
document, along with the similarity of already selected
keywords and keyphrases. This results in a selection of keywords
that maximize their within diversity with respect to the document.
Arguments:
doc_embedding: The document embeddings
word_embeddings: The embeddings of the selected candidate keywords/phrases
words: The selected candidate keywords/keyphrases
top_n: The number of keywords/keyhprases to return
diversity: How diverse the select keywords/keyphrases are.
Values between 0 and 1 with 0 being not diverse at all
and 1 being most diverse.
Returns:
List[Tuple[str, float]]: The selected keywords/keyphrases with their distances
"""
# Extract similarity within words, and between words and the document
word_doc_similarity = cosine_similarity(word_embeddings, doc_embedding)
word_similarity = cosine_similarity(word_embeddings)
# Initialize candidates and already choose best keyword/keyphras
keywords_idx = [np.argmax(word_doc_similarity)]
candidates_idx = [i for i in range(len(words)) if i != keywords_idx[0]]
for _ in range(min(top_n - 1, len(words) - 1)):
# Extract similarities within candidates and
# between candidates and selected keywords/phrases
candidate_similarities = word_doc_similarity[candidates_idx, :]
target_similarities = np.max(
word_similarity[candidates_idx][:, keywords_idx], axis=1
)
# Calculate MMR
mmr = (
1 - diversity
) * candidate_similarities - diversity * target_similarities.reshape(-1, 1)
mmr_idx = candidates_idx[np.argmax(mmr)]
# Update keywords & candidates
keywords_idx.append(mmr_idx)
candidates_idx.remove(mmr_idx)
# Extract and sort keywords in descending similarity
keywords = [
(words[idx], round(float(word_doc_similarity.reshape(1, -1)[0][idx]), 4))
for idx in keywords_idx
]
keywords = sorted(keywords, key=itemgetter(1), reverse=True)
return keywords